Repository: kmario23/deep-learning-drizzle Branch: master Commit: 226bd1d04456 Files: 7 Total size: 552.3 KB Directory structure: gitextract_jfdrh3ok/ ├── README.md └── markdown2html_py/ ├── README.md ├── fontawesome-animations.txt ├── html_foot.txt ├── html_head.txt ├── index.html └── markdown2html.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: README.md ================================================ # :balloon: :tada: Deep Learning Drizzle :confetti_ball: :balloon: :books: [**"Read enough so you start developing intuitions and then trust your intuitions and go for it!"** ](https://www.deeplearning.ai/hodl-geoffrey-hinton/) :books:
Prof. Geoffrey Hinton, University of Toronto :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ### Contents :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: | | | | ------------------------------------------------------------ | ------------------------------------------------------------ | | **Deep Learning (Deep Neural Networks)** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#tada-deep-learning-deep-neural-networks-confetti_ball-balloon) | **Probabilistic Graphical Models** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#loudspeaker-probabilistic-graphical-models-sparkles) | | | | | **Machine Learning Fundamentals** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#cupid-machine-learning-fundamentals-cyclone-boom) | **Natural Language Processing** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#hibiscus-natural-language-processing-cherry_blossom-sparkling_heart) | | | | | **Optimization for Machine Learning** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#cupid-optimization-for-machine-learning-cyclone-boom) | **Automatic Speech Recognition** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#speaking_head-automatic-speech-recognition-speech_balloon-thought_balloon) | | | | | **General Machine Learning** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#cupid-general-machine-learning-cyclone-boom) | **Modern Computer Vision** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#fire-modern-computer-vision-camera_flash-movie_camera) | | | | | **Reinforcement Learning** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#balloon-reinforcement-learning-hotsprings-video_game) | **Boot Camps or Summer Schools** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#star2-boot-camps-or-summer-schools-maple_leaf) | | | | | **Bayesian Deep Learning** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#game_die-bayesian-deep-learning-spades-gem) | **Medical Imaging** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#movie_camera-medical-imaging-camera-video_camera) | | | | | **Graph Neural Networks** [:arrow_heading_down: ](https://github.com/kmario23/deep-learning-drizzle#tada-graph-neural-networks-geometric-dl-confetti_ball-balloon) | **Bird's-eye view of Artificial Intelligence** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#bird-birds-eye-view-of-agi-eagle) | | | | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ## :tada: Deep Learning (Deep Neural Networks) :confetti_ball: :balloon: :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: | S.No | Course Name | University/Instructor(s) | Course WebPage | Lecture Videos | Year | | ---- | ----------------------------------------------------- | ---------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | --------------- | | 1. | **Neural Networks for Machine Learning** | Geoffrey Hinton, University of Toronto | [Lecture-Slides](http://www.cs.toronto.edu/~hinton/coursera_slides.html)
[CSC321-tijmen](https://www.cs.toronto.edu/~tijmen/csc321/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoRl3Ht4JOcdU872GhiYWf6jwrk_SNhz9)
[UofT-mirror](https://www.cs.toronto.edu/~hinton/coursera_lectures.html) | 2012
2014 | | 2. | **Neural Networks Demystified** | Stephen Welch, Welch Labs | [Suppl. Code](https://github.com/stephencwelch/Neural-Networks-Demystified) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU) | 2014 | | 3. | **Deep Learning at Oxford** | Nando de Freitas, Oxford University | [Oxford-ML](http://www.cs.ox.ac.uk/teaching/courses/2014-2015/ml/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu) | 2015 | | 4. | **Deep Learning for Perception** | Dhruv Batra, Virginia Tech | [ECE-6504](https://computing.ece.vt.edu/~f15ece6504/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL-fZD610i7yAsfH2eLBiRDa90kL2ML0f7) | 2015 | | 5. | **Deep Learning** | Ali Ghodsi, University of Waterloo | [STAT-946](https://uwaterloo.ca/data-analytics/deep-learning) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLehuLRPyt1Hyi78UOkMPWCGRxGcA9NVOE) | F2015 | | 6. | **CS231n: CNNs for Visual Recognition** | Andrej Karpathy, Stanford University | [CS231n](http://cs231n.stanford.edu/2015/) | `None` | 2015 | | 7. | **CS224d: Deep Learning for NLP** | Richard Socher, Stanford University | [CS224d](http://cs224d.stanford.edu) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLmImxx8Char8dxWB9LRqdpCTmewaml96q) | 2015 | | 8. | **Bay Area Deep Learning** | Many legends, Stanford | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLrAXtmErZgOfMuxkACrYnD2fTgbzk2THW) | 2016 | | 9. | **CS231n: CNNs for Visual Recognition** | Andrej Karpathy, Stanford University | [CS231n](http://cs231n.stanford.edu/2016/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC)
[(Academic Torrent)](https://academictorrents.com/details/46c5af9e2075d9af06f280b55b65cf9b44eb9fe7) | 2016 | | 10. | **Neural Networks** | Hugo Larochelle, Université de Sherbrooke | [Neural-Networks](http://info.usherbrooke.ca/hlarochelle/neural_networks/content.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH)
[(Academic Torrent)](https://academictorrents.com/details/e046bca3bc837053d1609ef33d623ee5c5af7300) | 2016 | | | | | | | | | 11. | **CS224d: Deep Learning for NLP** | Richard Socher, Stanford University | [CS224d](http://cs224d.stanford.edu) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLlJy-eBtNFt4CSVWYqscHDdP58M3zFHIG)
[(Academic Torrent)](https://academictorrents.com/details/dd9b74b50a1292b4b154094b7338ec1d66e8894d) | 2016 | | 12. | **CS224n: NLP with Deep Learning** | Richard Socher, Stanford University | [CS224n](http://web.stanford.edu/class/cs224n/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6) | 2017 | | 13. | **CS231n: CNNs for Visual Recognition** | Justin Johnson, Stanford University | [CS231n](http://cs231n.stanford.edu/2017/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv)
[(Academic Torrent)](https://academictorrents.com/details/ed8a16ebb346e14119a03371665306609e485f13) | 2017 | | 14. | **Topics in Deep Learning** | Ruslan Salakhutdinov, CMU | [10707](https://deeplearning-cmu-10707.github.io/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLpIxOj-HnDsOSL__Buy7_UEVQkyfhHapa) | F2017 | | 15. | **Deep Learning Crash Course** | Leo Isikdogan, UT Austin | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLWKotBjTDoLj3rXBL-nEIPRN9V3a9Cx07) | 2017 | | 16. | **Deep Learning and its Applications** | François Pitié, Trinity College Dublin | [EE4C16](https://github.com/frcs/4C16-2017) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLIo1iEzl5iB9NkulNR0X5vXN8AaEKglWT) | 2017 | | 17. | **Deep Learning** | Andrew Ng, Stanford University | [CS230](http://cs230.stanford.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rOABXSygHTsbvUz4G_YQhOb) | 2018 | | 18. | **UvA Deep Learning** | Efstratios Gavves, University of Amsterdam | [UvA-DLC](https://uvadlc.github.io/) | [Lecture-Videos](https://uvadlc.github.io/lectures-sep2018.html) | 2018 | | 19. | **Advanced Deep Learning and Reinforcement Learning** | Many legends, DeepMind | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs) | 2018 | | 20. | **Machine Learning** | Peter Bloem, Vrije Universiteit Amsterdam | [MLVU](https://mlvu.github.io/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLCof9EqayQgsORO3pFzeYZFz6cszYO0VJ) | 2018 | | | | | | | | | 21. | **Deep Learning** | Francois Fleuret, EPFL | [EE-59](https://fleuret.org/ee559-2018/dlc) | [Video-Lectures](https://fleuret.org/ee559-2018/dlc/#materials) | 2018 | | 22. | **Introduction to Deep Learning** | Alexander Amini, Harini Suresh and others, MIT | [6.S191](http://introtodeeplearning.com/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI)
[2017-version](https://www.youtube.com/playlist?list=PLkkuNyzb8LmxFutYuPA7B4oiMn6cjD6Rs) | 2017- 2021 | | 23. | **Deep Learning for Self-Driving Cars** | Lex Fridman, MIT | [6.S094](https://selfdrivingcars.mit.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf) | 2017-2018 | | 24. | **Introduction to Deep Learning** | Bhiksha Raj and many others, CMU | [11-485/785](http://deeplearning.cs.cmu.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLp-0K3kfddPwJBJ4Q8We-0yNQEG0fZrSa) | S2018 | | 25. | **Introduction to Deep Learning** | Bhiksha Raj and many others, CMU | [11-485/785](http://deeplearning.cs.cmu.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLp-0K3kfddPyH44FP0dl0CbYprvTcfgOI) [Recitation-Inclusive](https://www.youtube.com/playlist?list=PLLR0_ZOlbfD6KDBq93G8-guHI-J1ICeFm) | F2018 | | 26. | **Deep Learning Specialization** | Andrew Ng, Stanford | [DL.AI](https://www.deeplearning.ai/deep-learning-specialization/) | [YouTube-Lectures](https://www.youtube.com/channel/UCcIXc5mJsHVYTZR1maL5l9w/playlists) | 2017-2018 | | 27. | **Deep Learning** | Ali Ghodsi, University of Waterloo | [STAT-946](https://uwaterloo.ca/data-analytics/teaching/deep-learning-2017) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLehuLRPyt1HxTolYUWeyyIoxDabDmaOSB) | F2017 | | 28. | **Deep Learning** | Mitesh Khapra, IIT-Madras | [CS7015](https://www.cse.iitm.ac.in/~miteshk/CS7015.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLyqSpQzTE6M9gCgajvQbc68Hk_JKGBAYT) | 2018 | | 29. | **Deep Learning for AI** | UPC Barcelona | [DLAI-2017](https://telecombcn-dl.github.io/2017-dlai/)
[DLAI-2018](https://telecombcn-dl.github.io/2018-dlai/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL-5eMc3HQTBagIUjKefjcTbnXC0wXC_vd) | 2017-2018 | | 30. | **Deep Learning** | Alex Bronstein and Avi Mendelson, Technion | [CS236605](https://vistalab-technion.github.io/cs236605/info/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM0a6Z788YAZuqg2Ip-_dPLzEd33lZvP2) | 2018 | | | | | | | | | 31. | **MIT Deep Learning** | Many Researchers, Lex Fridman, MIT | [6.S094, 6.S091, 6.S093](https://deeplearning.mit.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf) | 2019 | | 32. | **Deep Learning Book** companion videos | Ian Goodfellow and others | [DL-book slides](https://www.deeplearningbook.org/lecture_slides.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLsXu9MHQGs8df5A4PzQGw-kfviylC-R9b) | 2017 | | 33. | **Theories of Deep Learning** | Many Legends, Stanford | [Stats-385](https://stats385.github.io/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLwUqqMt5en7fFLwSDa9V3JIkDam-WWgqy)
(first 10 lectures) | F2017 | | 34. | **Neural Networks** | Grant Sanderson | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi) | 2017-2018 | | 35. | **CS230: Deep Learning** | Andrew Ng, Kian Katanforoosh, Stanford | [CS230](http://cs230.stanford.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rOABXSygHTsbvUz4G_YQhOb) | A2018 | | 36. | **Theory of Deep Learning** | Lots of Legends, Canary Islands | [DALI'18](http://dalimeeting.org/dali2018/workshopTheoryDL.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLeCNfJWZKqxtWBnV8gefGqmmPgz9YF4LR) | 2018 | | 37. | **Introduction to Deep Learning** | Alex Smola, UC Berkeley | [Stat-157](http://courses.d2l.ai/berkeley-stat-157/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZSO_6-bSqHQHBCoGaObUljoXAyyqhpFW) | S2019 | | 38. | **Deep Unsupervised Learning** | Pieter Abbeel, UC Berkeley | [CS294-158](https://sites.google.com/view/berkeley-cs294-158-sp19/home) | [YouTube-Lectures](https://www.youtube.com/channel/UCf4SX8kAZM_oGcZjMREsU9w/videos) | S2019 | | 39. | **Machine Learning** | Peter Bloem, Vrije Universiteit Amsterdam | [MLVU](https://mlvu.github.io/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLCof9EqayQgupldnTvqNy_BThTcME5r93) | 2019 | | 40. | **Deep Learning on Computational Accelerators** | Alex Bronstein and Avi Mendelson, Technion | [CS236605](https://vistalab-technion.github.io/cs236605/lectures/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM0a6Z788YAa_WCy_V-q9NrGm5qQegZR5) | S2019 | | | | | | | | | 41. | **Introduction to Deep Learning** | Bhiksha Raj and many others, CMU | [11-785](http://www.cs.cmu.edu/~bhiksha/courses/deeplearning/Spring.2019/www) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLp-0K3kfddPzNdZPX4p0lVi6AcDXBofuf) | S2019 | | 42. | **Introduction to Deep Learning** | Bhiksha Raj and many others, CMU | [11-785](https://www.cs.cmu.edu/~bhiksha/courses/deeplearning/Fall.2019/www) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLp-0K3kfddPwz13VqV1PaMXF6V6dYdEsj)
[Recitations](https://www.youtube.com/playlist?list=PLp-0K3kfddPxf4T59JEQKv5UanLPVsxzz) | F2019 | | 43. | **UvA Deep Learning** | Efstratios Gavves, University of Amsterdam | [UvA-DLC](https://uvadlc.github.io/) | [Lecture-Videos](https://uvadlc.github.io/lectures-apr2019.html) | S2019 | | 44. | **Deep Learning** | Prabir Kumar Biswas, IIT Kgp | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLbRMhDVUMngc7NM-gDwcBzIYZNFSK2N1a) | 2019 | | 45. | **Deep Learning and its Applications** | Aditya Nigam, IIT Mandi | [CS-671](http://faculty.iitmandi.ac.in/~aditya/cs671/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLKvX2d3IUq586Ic9gIhZj6ubpWV-OJfl4) | 2019 | | 46. | **Neural Networks** | Neil Rhodes, Harvey Mudd College | [CS-152](https://www.cs.hmc.edu/~rhodes/cs152/schedule.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgEuVSRbAI9UIQSHGy4l01laA_12YOqEj) | F2019 | | 47. | **Deep Learning** | Thomas Hofmann, ETH Zürich | [DAL-DL](http://www.da.inf.ethz.ch/teaching/2019/DeepLearning) | [Lecture-Videos](https://video.ethz.ch/lectures/d-infk/2019/autumn/263-3210-00L.html) | F2019 | | 48. | **Deep Learning** | Milan Straka, Charles University | [NPFL114](https://ufal.mff.cuni.cz/courses/npfl114) | [Lecture-Videos](https://ufal.mff.cuni.cz/courses/npfl114/1718-summer) | S2019 | | 49. | **UvA Deep Learning** | Efstratios Gavves, University of Amsterdam | [UvA-DLC-19](https://uvadlc.github.io/#lectures) | [Lecture-Videos](https://uvadlc.github.io/#lectures) | F2019 | | 50. | **Artificial Intelligence: Principles and Techniques** | Percy Liang and Dorsa Sadigh, Stanford University | [CS221](https://stanford-cs221.github.io/autumn2019/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rO1NB9TD4iUZ3qghGEGtqNX) | F2019 | | | | | | | | | 51. | **Analyses of Deep Learning** | Lots of Legends, Stanford University | [STATS-385](https://stats385.github.io/) | [YouTube-Lectures](https://stats385.github.io/lecture_videos) | 2017-2019 | | 52. | **Deep Learning Foundations and Applications** | Debdoot Sheet and Sudeshna Sarkar, IIT-Kgp | [AI61002](http://www.facweb.iitkgp.ac.in/~debdoot/courses/AI61002/Spr2020) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_AdDfjIMo6pZfwjZ0rJlkE_MIsmRW7Mh) | S2020 | | 53. | **Designing, Visualizing, and Understanding Deep Neural Networks** | John Canny, UC Berkeley | [CS 182/282A](https://bcourses.berkeley.edu/courses/1487769/pages/cs-l-w-182-slash-282a-designing-visualizing-and-understanding-deep-neural-networks-spring-2020) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLkFD6_40KJIwaO6Eca8kzsEFBob0nFvwm) | S2020 | | 54. | **Deep Learning** | Yann LeCun and Alfredo Canziani, NYU | [DS-GA 1008](https://atcold.github.io/pytorch-Deep-Learning/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq) | S2020 | | 55. | **Introduction to Deep Learning** | Bhiksha Raj, CMU | [11-785](https://deeplearning.cs.cmu.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLp-0K3kfddPzCnS4CqKphh-zT3aDwybDe) | S2020 | | 56. | **Deep Unsupervised Learning** | Pieter Abbeel, UC Berkeley | [CS294-158](https://sites.google.com/view/berkeley-cs294-158-sp20) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLwRJQ4m4UJjPiJP3691u-qWwPGVKzSlNP) | S2020 | | 57. | **Machine Learning** | Peter Bloem, Vrije Universiteit Amsterdam | [VUML](https://mlvu.github.io/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLCof9EqayQgthR7IViXkAkUwel_rhxGYM) | S2020 | | 58. | **Deep Learning (with PyTorch)** | Alfredo Canziani and Yann LeCun, NYU | [DS-GA 1008](https://atcold.github.io/pytorch-Deep-Learning/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq) | S2020 | | 59. | **Introduction to Deep Learning and Generative Models** | Sebastian Raschka, UW-Madison | [Stat453](http://pages.stat.wisc.edu/~sraschka/teaching/stat453-ss2020/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTKMiZHVd_2JkR6QtQEnml7swCnFBtq4P) | S2020 | | 60. | **Deep Learning** | Andreas Maier, FAU Erlangen-Nürnberg | [DL-2020](https://www.video.uni-erlangen.de/course/id/925) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLpOGQvPCDQzvgpD3S0vTy7bJe2pf_yJFj)
[Lecture-Videos](https://www.video.uni-erlangen.de/course/id/925) | SS2020 | | | | | | | | | 61. | **Introduction to Deep Learning** | Laura Leal-Taixé and Matthias Niessner, TU-München | [I2DL-IN2346](https://dvl.in.tum.de/teaching/i2dl-ss20/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLQ8Y4kIIbzy_OaXv86lfbQwPHSomk2o2e) | SS2020 | | 62. | **Deep Learning** | Sargur Srihari, SUNY-Buffalo | [CSE676](https://cedar.buffalo.edu/~srihari/CSE676/) | [YouTube-Lectures-P1](https://www.youtube.com/playlist?list=PLmx4utxjUQD70k_NzeiSIXf30m54T_e1h)
[YouTube-Lectures-P2](https://www.youtube.com/channel/UCUm7yUmVJyAbYh_0ppJ4H-g/videos) | 2020 | | 63. | **Deep Learning Lecture Series** | Lots of Legends, DeepMind x UCL, London | [DLLS-20](https://deepmind.com/learning-resources/deep-learning-lecture-series-2020) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLqYmG7hTraZCDxZ44o4p3N5Anz3lLRVZF) | 2020 | | 64. | **MultiModal Machine Learning** | Louis-Philippe Morency & others, Carnegie Mellon University | [11-777 MMML-20](https://cmu-multicomp-lab.github.io/mmml-course/fall2020) | [YouTube-Lectures](https://www.youtube.com/channel/UCqlHIJTGYhiwQpNuPU5e2gg/videos) | F2020 | | 65. | **Reliable and Interpretable Artificial Intelligence** | Martin Vechev, ETH Zürich | [RIAI-20](https://www.sri.inf.ethz.ch/teaching/riai2020) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLWjm4hHpaNg6c-W7JjNYDEC_kJK9oSp0Y) | F2020 | | 66. | **Fundamentals of Deep Learning** | David McAllester, Toyota Technological Institute, Chicago | [TTIC-31230](https://mcallester.github.io/ttic-31230/Fall2020) | [YouTube-Lectures](https://www.youtube.com/channel/UCciVrtrRR3bQdaGbti9-hVQ/videos) | F2020 | | 67. | **Foundations of Deep Learning** | Soheil Feize, University of Maryland, College Park | [CMSC 828W](http://www.cs.umd.edu/class/fall2020/cmsc828W) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLHgjs9ncvHi80UCSlSvQe-TK_uOyDv_Jf) | F2020 | | 68. | **Deep Learning** | Andreas Geiger, Universität Tübingen | [DL-UT](https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/autonomous-vision/teaching/lecture-deep-learning/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij3NTWIdtMbfvX7Z-4WEXRqD) | W20/21 | | 69. | **Deep Learning** | Andreas Maier, FAU Erlangen-Nürnberg | [DL-FAU](https://www.fau.tv/course/id/1599) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLpOGQvPCDQzvJEPFUQ3mJz72GJ95jyZTh) | W20/21 | | 70. | **Fundamentals of Deep Learning** | Terence Parr and Yannet Interian, University of San Francisco | [DL-Fundamentals](https://github.com/parrt/fundamentals-of-deep-learning) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLFCc_Fc116ikeol9CZcWWKqmrJljxhE4N) | S2021 | | | | | | | | | 71. | **Full Stack Deep Learning** | Pieter Abbeel, Sergey Karayev, UC Berkeley | [FS-DL](https://fullstackdeeplearning.com/spring2021) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL1T8fO7ArWlcWg04OgNiJy91PywMKT2lv) | S2021 | | 72. | **Deep Learning: Designing, Visualizing, and Understanding DNNs** | Sergey Levine, UC Berkeley | [CS 182](https://cs182sp21.github.io) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_iWQOsE6TfVmKkQHucjPAoRtIJYt8a5A) | S2021 | | 73. | **Deep Learning in the Life Sciences** | Manolis Kellis, MIT | [6.874](https://mit6874.github.io) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLypiXJdtIca5sxV7aE3-PS9fYX3vUdIOX) | S2021 | | 74. | **Introduction to Deep Learning and Generative Models** | Sebastian Raschka, University of Wisconsin-Madison | [Stat 453](http://pages.stat.wisc.edu/~sraschka/teaching/stat453-ss2021) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTKMiZHVd_2KJtIXOW0zFhFfBaJJilH51) | S2021 | | 75. | **Deep Learning** | Alfredo Canziani and Yann LeCun, NYU | [NYU-DLSP21](https://atcold.github.io/NYU-DLSP21) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLLHTzKZzVU9e6xUfG10TkTWApKSZCzuBI) | S2021 | | 76. | **Applied Deep Learning** | Alexander Pacha, TU Wien | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLNsFwZQ_pkE8xNYTEyorbaWPN7nvbWyk1) | 2020-2021 | | 77. | **Machine Learning** | Hung-yi Lee, National Taiwan University | [ML'21](https://speech.ee.ntu.edu.tw/~hylee/ml/2021-spring.php) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLJV_el3uVTsNxV_IGauQZBHjBKZ26JHjd) | S2021 | | 78. | **Mathematics of Deep Learning** | Lots of legends, FAU | [MoDL](https://www.fau.tv/course/id/878) | [Lecture-Videos](https://www.fau.tv/course/id/878) | 2019-21 | | 79. | **Deep Learning** | Peter Bloem, Michael Cochez, and Jakub Tomczak, VU-Amsterdam | [DL](https://dlvu.github.io/) | [YouTube-Lectures](https://www.youtube.com/channel/UCYh1zKnwzrSjrO2Ae-akfTg/playlists) | 2020-21 | | 80. | **Applied Deep Learning** | Maziar Raissi, UC Boulder | [ADL'21](https://github.com/maziarraissi/Applied-Deep-Learning) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoEMreTa9CNmuxQeIKWaz7AVFd_ZeAcy4) | 2021 | | | | | | | | | 81. | **An Introduction to Group Equivariant Deep Learning** | Erik J. Bekkers, Universiteit van Amsterdam | [UvAGEDL](https://uvagedl.github.io) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL8FnQMH2k7jzPrxqdYufoiYVHim8PyZWd) | 2022 | | | | | | | | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ### :cupid: Machine Learning Fundamentals :cyclone: :boom: :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: | S.No | Course Name | University/Instructor(s) | Course Webpage | Video Lectures | Year | | ---- | ------------------------------------------------------------ | ------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ---------- | | 1. | **Linear Algebra** | Gilbert Strang, MIT | [18.06 SC](http://ocw.mit.edu/18-06SCF11) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL221E2BBF13BECF6C) | 2011 | | 2. | **Probability Primer** | Jeffrey Miller, Brown University | `mathematical monk` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL17567A1A3F5DB5E4) | 2011 | | 3. | **Information Theory, Pattern Recognition, and Neural Networks** | David Mackay, University of Cambridge | [ITPRNN](http://www.inference.org.uk/mackay/itprnn) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLruBu5BI5n4aFpG32iMbdWoRVAA-Vcso6) | 2012 | | 4. | **Linear Algebra Review** | Zico Kolter, CMU | [LinAlg](http://www.cs.cmu.edu/~zkolter/course/linalg/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM4Pv4KYYzGzL5ay6dmpyzRnbzQ__8v_t) | 2013 | | 5. | **Probability and Statistics** | Michel van Biezen | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLX2gX-ftPVXUWwTzAkOhBdhplvz0fByqV) | 2015 | | 6. | **Linear Algebra: An in-depth Introduction** | Pavel Grinfeld | `None` | [Part-1](https://www.youtube.com/playlist?list=PLlXfTHzgMRUKXD88IdzS14F4NxAZudSmv)
[Part-2](https://www.youtube.com/playlist?list=PLlXfTHzgMRULWJYthculb2QWEiZOkwTSU)
[Part-3](https://www.youtube.com/playlist?list=PLlXfTHzgMRUIqYrutsFXCOmiqKUgOgGJ5)
[Part-4](https://www.youtube.com/playlist?list=PLlXfTHzgMRULZfrNCrrJ7xDcTjGr633mm) | 2015- 2017 | | 7. | **Multivariable Calculus** | Grant Sanderson, Khan Academy | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLSQl0a2vh4HC5feHa6Rc5c0wbRTx56nF7) | 2016 | | 8. | **Essence of Linear Algebra** | Grant Sanderson | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) | 2016 | | 9. | **Essence of Calculus** | Grant Sanderson | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr) | 2017-2018 | | 10. | **Math Background for Machine Learning** | Geoff Gordon, CMU | [10-606](https://canvas.cmu.edu/courses/603/assignments/syllabus), [10-607](https://piazza.com/cmu/fall2017/1060610607/home) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL7y-1rk2cCsAqRtWoZ95z-GMcecVG5mzA) | F2017 | | | | | | | | | 11. | **Mathematics for Machine Learning** (Linear Algebra, Calculus) | David Dye, Samuel Cooper, and Freddie Page, IC-London | [MML](https://www.coursera.org/learn/linear-algebra-machine-learning) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLmAuaUS7wSOP-iTNDivR0ANKuTUhEzMe4) | 2018 | | 12. | **Multivariable Calculus** | S.K. Gupta and Sanjeev Kumar, IIT-Roorkee | [MVC](https://nptel.ac.in/syllabus/111107108/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLq-Gm0yRYwTiQtK374NzhFOcQkWmJ71vx) | 2018 | | 13. | **Engineering Probability** | Rich Radke, Rensselaer Polytechnic Institute | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLuh62Q4Sv7BU1dN2G6ncyiMbML7OXh_Jx) | 2018 | | 14. | **Matrix Methods in Data Analysis, Signal Processing, and Machine Learning** | Gilbert Strang, MIT | [18.065](https://ocw.mit.edu/courses/mathematics/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k) | S2018 | | 15. | **Information Theory** | Himanshu Tyagi, IISC, Bengaluru | [E2 201](https://ece.iisc.ac.in/~htyagi/course-E2201-2020.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgMDNELGJ1CYS-8dlMGPIaowVfeda4nUj) | 2018-20 | | 16. | **Math Camp** | Mark Walker, University of Arizona | [UAMathCamp / Econ-519](http://www.u.arizona.edu/~mwalker/MathCamp2019.htm) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLcjqUUQt__ZGLhwUacPm7_RKs2eJNFwco) | 2019 | | 17. | **A 2020 Vision of Linear Algebra** | Gilbert Strang, MIT | [VoLA](https://ocw.mit.edu/resources/res-18-010-a-2020-vision-of-linear-algebra-spring-2020/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLUl4u3cNGP61iQEFiWLE21EJCxwmWvvek) | S2020 | | 18. | **Mathematics for Numerical Computing and Machine Learning** | Szymon Rusinkiewicz, Princeton University | [COS-302](https://www.cs.princeton.edu/courses/archive/fall20/cos302/outline.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL88aSuXxl_dSjC5pIG8bGkC5wsUPyW_Hh) | F2020 | | 19. | **Essential Statistics for Neuroscientists** | Philipp Berens, Universität Klinikum Tübingen | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij0Gw5SLIrOA1dMYScCx4oXT) | 2020 | | 20. | **Mathematics for Machine Learning** | Ulrike von Luxburg, Eberhard Karls Universität Tübingen | [Math4ML](https://www.tml.cs.uni-tuebingen.de/teaching/2020_maths_for_ml) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij1a6KdEy8PVE9zoCv6SlHRS) | W2020 | | 21. | **Introduction to Causal Inference** | Brady Neal, Mila, Montréal | [CausalInf](https://www.bradyneal.com/causal-inference-course) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0) | F2020 | | 22. | **Applied Linear Algebra** | Andrew Thangaraj, IIT Madras | [EE5120](http://www.ee.iitm.ac.in/~andrew/EE5120) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLyqSpQzTE6M-CHZU5RGfamcXOnuFyTOpm) | 2021 | | 23. | **Mathematical Tools for Data Science** | Carlos Fernandez-Granda, New York University | [DS-GA 1013/Math-GA 2824](https://cds.nyu.edu/math-tools) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLBEf5mJtE6KtU6YlXFZD6lyYcHhW5pIlc) | 2021 | | 24. | **Mathematics for Numerical Computing and Machine Learning** | Ryan Adams, Princeton University | [COS 302 / SML 305](https://www.cs.princeton.edu/courses/archive/spring21/cos302) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLCO4cUaBLHFEHo42HVIVWaSOvbAiH30uc) | 2021 | | | | | | | | | | | | | | | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ### :cupid: Optimization for Machine Learning :cyclone: :boom: :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: | S.No | Course Name | University/Instructor(s) | Course Webpage | Video Lectures | Year | | ---- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ---------- | | 1. | **Convex Optimization** | Stephen Boyd, Stanford University | [ee364a](http://web.stanford.edu/class/ee364a/lectures.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL3940DD956CDF0622) | 2008 | | 2. | **Introduction to Optimization** | Michael Zibulevsky, Technion | [CS-236330](https://sites.google.com/site/michaelzibulevsky/optimization-course) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLDFB2EEF4DDAFE30B) | 2009 | | 3. | **Optimization for Machine Learning** | S V N Vishwanathan, Purdue University | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL09B0E8AFC69BE108) | 2011 | | 4. | **Optimization** | Geoff Gordon & Ryan Tibshirani, CMU | [10-725](https://www.cs.cmu.edu/~ggordon/10725-F12/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL7y-1rk2cCsDOv91McLOnV4kExFfTB7dU) | 2012 | | 5. | **Convex Optimization** | Joydeep Dutta, IIT-Kanpur | [cvx-nptel](https://nptel.ac.in/courses/111/104/111104068) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLbMVogVj5nJQHFqfiSdgaLCCWvDcm1W4l) | 2013 | | 6. | **Foundations of Optimization** | Joydeep Dutta, IIT-Kanpur | [fop-nptel](https://nptel.ac.in/courses/111/104/111104071) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLbMVogVj5nJRRbofh3Qm3P6_NVyevDGD_) | 2014 | | 7. | **Algorithmic Aspects of Machine Learning** | Ankur Moitra, MIT | [18.409-AAML](http://people.csail.mit.edu/moitra/409.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLB3sDpSRdrOvI1hYXNsa6Lety7K8FhPpx) | S2015 | | 8. | **Numerical Optimization** | Shirish K. Shevade, IISC | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL6EA0722B99332589) | 2015 | | 9. | **Convex Optimization** | Ryan Tibshirani, CMU | [10-725](https://www.stat.cmu.edu/~ryantibs/convexopt-S15/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLjbUi5mgii6BZBhJ9nW7eydgycyCOYeZ6) | S2015 | | 10. | **Convex Optimization** | Ryan Tibshirani, CMU | [10-725](http://stat.cmu.edu/~ryantibs/convexopt-F15/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLjbUi5mgii6AGJW3La3BpEXe27n8v3biT) | F2015 | | 11. | **Advanced Algorithms** | Ankur Moitra, MIT | [6.854-AA](http://people.csail.mit.edu/moitra/854.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL6ogFv-ieghdoGKGg2Bik3Gl1glBTEu8c) | S2016 | | 12. | **Introduction to Optimization** | Michael Zibulevsky, Technion | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLBD31626529B0AC2A) | 2016 | | 13. | **Convex Optimization** | Javier Peña & Ryan Tibshirani | [10-725/36-725](https://www.stat.cmu.edu/~ryantibs/convexopt-F16) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLjbUi5mgii6AVdvImLB9-Hako68p9MpIC) | F2016 | | 14. | **Convex Optimization** | Ryan Tibshirani, CMU | [10-725](https://www.stat.cmu.edu/~ryantibs/convexopt-F18/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLpIxOj-HnDsMM7BCNGC3hPFU3DfCWfVIw)
[Lecture-Videos](https://www.stat.cmu.edu/~ryantibs/convexopt-F18/) | F2018 | | 15. | **Modern Algorithmic Optimization** | Yurii Nesterov, UCLouvain | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLEqoHzpnmTfAoUDqnmMly-KgyJ6ZM_axf) | 2018 | | 16. | **Optimization, Foundations of Optimization** | Mark Walker, University of Arizona | [MathCamp-20](http://www.u.arizona.edu/~mwalker/MathCamp2020/MathCamp2020LectureNotes.htm) | [YouTube-Lectures-Found.](https://www.youtube.com/playlist?list=PLcjqUUQt__ZE6wp_c4-FcRdmzBvx8VN7O)
[YouTube-Lectures-Opt](https://www.youtube.com/playlist?list=PLcjqUUQt__ZE0ZSTNRyBIgLJ5obPHdmxC) | 2019 - now | | 17. | **Optimization: Principles and Algorithms** | Michel Bierlaire, École polytechnique fédérale de Lausanne (EPFL) | [opt-algo](https://transp-or.epfl.ch/books/optimization/html/about_book.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM4Pv4KYYzGzOpWwsaV6GgllT6njsi1G-) | 2019 | | 18. | **Optimization and Simulation** | Michel Bierlaire, École polytechnique fédérale de Lausanne (EPFL) | [opt-sim](https://transp-or.epfl.ch/courses/OptSim2019/slides.php) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL10NOnsbP5Q5NlJ-Y6Eiup6RTSfkuj1TR) | S2019 | | 19. | **Brazilian Workshop on Continuous Optimization** | Lots of Legends, Instituto Nacional de Matemática Pura e Aplicada, Rio de Janeiro | [cont. opt.](https://impa.br/eventos-do-impa/eventos-2019/xiii-brazilian-workshop-on-continuous-optimization) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLo4jXE-LdDTQVZhnLPq2W31vJ1fq1VSp6) | 2019 | | 20. | **One World Optimization Seminar** | Lots of Legends, Universität Wien | [1W-OPT](https://owos.univie.ac.at) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLBQo-yZOMzLWEcAptzTYOnwXo9hhXrAa2) | 2020- | | | | | | | | | 21. | **Convex Optimization II** | Constantine Caramanis, UT Austin | [CVX-Optim-II](http://users.ece.utexas.edu/~cmcaram/constantine_caramanis/Announcements.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLXsmhnDvpjORzPelSDs0LSDrfJcqyLlZc) | S2020 | | 22. | **Combinatorial Optimization** | Constantine Caramanis, UT Austin | [comb-op](https://caramanis.github.io/teaching/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLXsmhnDvpjORcTRFMVF3aUgyYlHsxfhNL) | F2020 | | 23. | **Optimization Methods for Machine Learning and Engineering** | Julius Pfrommer, Jürgen Beyerer, Karlsruher Institut für Technologie (KIT) | [Optim-MLE](https://ies.iar.kit.edu/lehre_1487.php), [slides](https://drive.google.com/drive/folders/1WWVWV4vDBIOkjZc6uFY3nfXvpaOUHcfb) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdkTDauaUnQpzuOCZyUUZc0lxf4-PXNR5) | W2020-21 | | | | | | | | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ### :cupid: General Machine Learning :cyclone: :boom: :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: | S.No | Course Name | University/Instructor(s) | Course Webpage | Video Lectures | Year | | ---- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | --------- | | 1. | **CS229: Machine Learning** | Andrew Ng, Stanford University | [CS229-old](https://see.stanford.edu/Course/CS229/)
[CS229-new](http://cs229.stanford.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLA89DCFA6ADACE599) | 2007 | | 2. | **Machine Learning** | Jeffrey Miller, Brown University | `mathematical monk` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLD0F06AA0D2E8FFBA) | 2011 | | 3. | **Machine Learning** | Tom Mitchell, CMU | [10-701](http://www.cs.cmu.edu/~tom/10701_sp11/) | [Lecture-Videos](http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml) | 2011 | | 4. | **Machine Learning and Data Mining** | Nando de Freitas, University of British Columbia | [CPSC-340](https://www.cs.ubc.ca/~nando/340-2012/index.php) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLE6Wd9FR--Ecf_5nCbnSQMHqORpiChfJf) | 2012 | | 5. | **Learning from Data** | Yaser Abu-Mostafa, CalTech | [CS156](http://work.caltech.edu/telecourse.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLD63A284B7615313A) | 2012 | | 6. | **Machine Learning** | Rudolph Triebel, Technische Universität München | [Machine Learning](https://vision.in.tum.de/teaching/ws2013/ml_ws13) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTBdjV_4f-EIiongKlS9OKrBEp8QR47Wl) | 2013 | | 7. | **Introduction to Machine Learning** | Alex Smola, CMU | [10-701](http://alex.smola.org/teaching/cmu2013-10-701/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZSO_6-bSqHQmMKwWVvYwKreGu4b4kMU9) | 2013 | | 8. | **Introduction to Machine Learning** | Alex Smola and Geoffrey Gordon, CMU | [10-701x](http://alex.smola.org/teaching/cmu2013-10-701x/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZSO_6-bSqHR7NPk4k0zqdm2dPdraQZ_B) | 2013 | | 9. | **Pattern Recognition** | Sukhendu Das, IIT-M and C.A. Murthy, ISI-Calcutta | [PR-NPTEL](https://nptel.ac.in/syllabus/106106046/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLbMVogVj5nJQJMLb2CYw9rry0d5s0TQRp) | 2014 | | 10. | **An Introduction to Statistical Learning with Applications in R** | Trevor Hastie and Robert Tibshirani, Stanford | [stat-learn](https://lagunita.stanford.edu/courses/HumanitiesandScience/StatLearning/Winter2015/about)
[R-bloggers](https://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLOg0ngHtcqbPTlZzRHA2ocQZqB1D_qZ5V) | 2014 | | | | | | | | | 11. | **Introduction to Machine Learning** | Katie Malone, Sebastian Thrun, Udacity | [ML-Udacity](https://www.udacity.com/course/ud120) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLAwxTw4SYaPkQXg8TkVdIvYv4HfLG7SiH) | 2015 | | 12. | **Introduction to Machine Learning** | Dhruv Batra, Virginia Tech | [ECE-5984](https://filebox.ece.vt.edu/~s15ece5984/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL-fZD610i7yDUiNTFy-tEOxkTwg4mHZHu) | 2015 | | 13. | **Statistical Learning - Classification** | Ali Ghodsi, University of Waterloo | [STAT-441](https://uwaterloo.ca/data-analytics/statistical-learning-classification) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLehuLRPyt1Hy-4ObWBK4Ab0xk97s6imfC) | 2015 | | 14. | **Machine Learning Theory** | Shai Ben-David, University of Waterloo | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLPW2keNyw-usgvmR7FTQ3ZRjfLs5jT4BO) | 2015 | | 15. | **Introduction to Machine Learning** | Alex Smola, CMU | [10-701](http://alex.smola.org/teaching/10-701-15/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZSO_6-bSqHTTV7w9u7grTXBHMH-mw3qn) | S2015 | | 16. | **Statistical Machine Learning** | Larry Wasserman, CMU | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLjbUi5mgii6BWEUZf7He6nowWvGne_Y8r) | S2015 | | 17. | **ML: Supervised Learning** | Michael Littman, Charles Isbell, Pushkar Kolhe, GaTech | [ML-Udacity](https://eu.udacity.com/course/machine-learning--ud262) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLAwxTw4SYaPl0N6-e1GvyLp5-MUMUjOKo) | 2015 | | 18. | **ML: Unsupervised Learning** | Michael Littman, Charles Isbell, Pushkar Kolhe, GaTech | [ML-Udacity](https://eu.udacity.com/course/machine-learning-unsupervised-learning--ud741) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLAwxTw4SYaPmaHhu-Lz3mhLSj-YH-JnG7) | 2015 | | 19. | **Advanced Introduction to Machine Learning** | Barnabas Poczos and Alex Smola | [10-715](https://www.cs.cmu.edu/~bapoczos/Classes/ML10715_2015Fall/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL4YhK0pT0ZhWBzSBkMGzpnPw6sf6Ma0IX) | F2015 | | 20. | **Machine Learning** | Pedro Domingos, UWashington | [CSEP-546](https://courses.cs.washington.edu/courses/csep546/16sp/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTPQEx-31JXgtDaC6-3HxWcp7fq4N8YGr) | S2016 | | | | | | | | | 21. | **Statistical Machine Learning** | Larry Wasserman, CMU | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTB9VQq8WiaCBK2XrtYn5t9uuPdsNm7YE) | S2016 | | 22. | **Machine Learning with Large Datasets** | William Cohen, CMU | [10-605](http://curtis.ml.cmu.edu/w/courses/index.php/Machine_Learning_with_Large_Datasets_10-605_in_Fall_2016) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLnfBqXRW5MRhPtfkadfwQ0VcuSi2IwEcW) | F2016 | | 23. | **Math Background for Machine Learning** | Geoffrey Gordon, CMU | `10-600` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL7y-1rk2cCsA339crwXMWUaBRuLBvPBCg) | F2016 | | 24. | **Statistical Learning - Classification** | Ali Ghodsi, University of Waterloo | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG) | 2017 | | 25. | **Machine Learning** | Andrew Ng, Stanford University | [Coursera-ML](https://www.coursera.org/learn/machine-learning) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN) | 2017 | | 26. | **Machine Learning** | Roni Rosenfield, CMU | [10-601](http://www.cs.cmu.edu/~roni/10601-f17/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL7k0r4t5c10-g7CWCnHfZOAxLaiNinChk) | 2017 | | 27. | **Statistical Machine Learning** | Ryan Tibshirani, Larry Wasserman, CMU | [10-702](http://www.stat.cmu.edu/~ryantibs/statml/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLjbUi5mgii6B7A0nM74zHTOVQtTC9DaCv) | S2017 | | 28. | **Machine Learning for Computer Vision** | Fred Hamprecht, Heidelberg University | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLuRaSnb3n4kSQFyt8VBldsQ9pO9Xtu8rY) | F2017 | | 29. | **Math Background for Machine Learning** | Geoffrey Gordon, CMU | [10-606 / 10-607](https://canvas.cmu.edu/courses/603/assignments/syllabus) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL7y-1rk2cCsAqRtWoZ95z-GMcecVG5mzA) | F2017 | | 30. | **Data Visualization** | Ali Ghodsi, University of Waterloo | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLehuLRPyt1HzQoXEhtNuYTmd0aNQvtyAK) | 2017 | | | | | | | | | 31. | **Machine Learning for Physicists** | Florian Marquardt, Uni Erlangen-Nürnberg | [ML4Phy-17](http://www.thp2.nat.uni-erlangen.de/index.php/2017_Machine_Learning_for_Physicists,_by_Florian_Marquardt) | [Lecture-Videos](https://www.video.uni-erlangen.de/course/id/574) | 2017 | | 32. | **Machine Learning for Intelligent Systems** | Kilian Weinberger, Cornell University | [CS4780](http://www.cs.cornell.edu/courses/cs4780/2018fa/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLl8OlHZGYOQ7bkVbuRthEsaLr7bONzbXS) | F2018 | | 33. | **Statistical Learning Theory and Applications** | Tomaso Poggio, Lorenzo Rosasco, Sasha Rakhlin | [9.520/6.860](https://cbmm.mit.edu/lh-9-520) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLyGKBDfnk-iAtLO6oLW4swMiQGz4f2OPY) | F2018 | | 34. | **Machine Learning and Data Mining** | Mike Gelbart, University of British Columbia | [CPSC-340](https://ubc-cs.github.io/cpsc340/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLWmXHcz_53Q02ZLeAxigki1JZFfCO6M-b) | 2018 | | 35. | **Foundations of Machine Learning** | David Rosenberg, Bloomberg | [FOML](https://bloomberg.github.io/foml/#home) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLnZuxOufsXnvftwTB1HL6mel1V32w0ThI) | 2018 | | 36. | **Introduction to Machine Learning** | Andreas Krause, ETH Zürich | [IntroML](https://las.inf.ethz.ch/teaching/introml-s18) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLzn6LN6WhlN273tsqyfdrBUsA-o5nUESV) | 2018 | | 37. | **Machine Learning Fundamentals** | Sanjoy Dasgupta, UC-San Diego | [MLF-slides](https://drive.google.com/drive/folders/1l1rwv-jMihLZIpW0zTgGN9-snWOsA3M9) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_onPhFCkVQhUzcTVgQiC8W2ShZKWlm0s) | 2018 | | 38. | **Machine Learning** | Jordan Boyd-Graber, University of Maryland | [CMSC-726](http://users.umiacs.umd.edu/~jbg/teaching/CMSC_726/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLegWUnz91WfsELyRcZ7d1GwAVifDaZmgo) | 2015-2018 | | 39. | **Machine Learning** | Andrew Ng, Stanford University | [CS229](http://cs229.stanford.edu/syllabus-autumn2018.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU) | 2018 | | 40. | **Machine Intelligence** | H.R.Tizhoosh, UWaterloo | [SYDE-522](https://kimialab.uwaterloo.ca/kimia/index.php/teaching/syde-522-machine-intelligence-2) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL4upCU5bnihwCX93Gv6AQnKmVMwx4AZoT) | 2019 | | | | | | | | | 41. | **Introduction to Machine Learning** | Pascal Poupart, University of Waterloo | [CS480/680](https://cs.uwaterloo.ca/~ppoupart/teaching/cs480-spring19) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdAoL1zKcqTW-uzoSVBNEecKHsnug_M0k) | S2019 | | 42. | **Advanced Machine Learning** | Thorsten Joachims, Cornell University | [CS-6780](https://www.cs.cornell.edu/courses/cs6780/2019sp) | [Lecture-Videos](https://cornell.mediasite.com/Mediasite/Catalog/Full/f5d1cd3323f746cca80b2468bf97efd421) | S2019 | | 43. | **Machine Learning for Structured Data** | Matt Gormley, Carnegie Mellon University | [10-418/10-618](http://www.cs.cmu.edu/~mgormley/courses/10418/schedule.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL4CxkUJbvNVihRKP4bXufvRLIWzeS-ieP) | F2019 | | 44. | **Advanced Machine Learning** | Joachim Buhmann, ETH Zürich | [ML2-AML](https://ml2.inf.ethz.ch/courses/aml/) | [Lecture-Videos](https://video.ethz.ch/lectures/d-infk/2019/autumn/252-0535-00L.html) | F2019 | | 45. | **Machine Learning for Signal Processing** | Vipul Arora, IIT-Kanpur | [MLSP](http://home.iitk.ac.in/~vipular/stuff/2019_MLSP.html) | [Lecture-Videos](https://iitk-my.sharepoint.com/:f:/g/personal/vipular_iitk_ac_in/Enf97NZfsoVBiyclC6yHfe4BlUv6CA4U8LPQQ4vtsDo_Xg) | F2019 | | 46. | **Foundations of Machine Learning** | Animashree Anandkumar, CalTech | [CMS-165](http://tensorlab.cms.caltech.edu/users/anima/cms165-2019.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLVNifWxslHCA5GUh0o92neMiWiQiGVFqp) | 2019 | | 47. | **Machine Learning for Physicists** | Florian Marquardt, Uni Erlangen-Nürnberg | `None` | [Lecture-Videos](https://www.video.uni-erlangen.de/course/id/778) | 2019 | | 48. | **Applied Machine Learning** | Andreas Müller, Columbia University | [COMS-W4995](https://www.cs.columbia.edu/~amueller/comsw4995s19/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_pVmAaAnxIQGzQS2oI3OWEPT-dpmwTfA) | 2019 | | 49. | **Fundamentals of Machine Learning over Networks** | Hossein Shokri-Ghadikolaei, KTH, Sweden | [MLoNs](https://sites.google.com/view/mlons/course-materials) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLWoZTd81WFCEBFrxDfNUrDnt3ABdLfg80) | 2019 | | 50. | **Foundations of Machine Learning and Statistical Inference** | Animashree Anandkumar, CalTech | [CMS-165](http://tensorlab.cms.caltech.edu/users/anima/cms165-2020.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLVNifWxslHCDlbyitaLLYBOAEPbmF1AHg) | 2020 | | | | | | | | | 51. | **Machine Learning** | Rebecca Willett and Yuxin Chen, University of Chicago | [STAT 37710 / CMSC 35400](https://voices.uchicago.edu/willett/teaching/stats37710-cmsc35400-s20) | [Lecture-Videos](https://voices.uchicago.edu/willett/teaching/stats37710-cmsc35400-s20) | S2020 | | 52. | **Introduction to Machine Learning** | Sanjay Lall and Stephen Boyd, Stanford University | [EE104/CME107](http://ee104.stanford.edu) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rN_Uy7_wmS051_q1d6akXmK) | S2020 | | 53. | **Applied Machine Learning** | Andreas Müller, Columbia University | [COMS-W4995](https://www.cs.columbia.edu/~amueller/comsw4995s20/schedule/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_pVmAaAnxIRnSw6wiCpSvshFyCREZmlM) | S2020 | | 54. | **Statistical Machine Learning** | Ulrike von Luxburg, Eberhard Karls Universität Tübingen | [Stat-ML](https://www.tml.cs.uni-tuebingen.de/teaching/2020_statistical_learning/index.php) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij2XCvrRzLokX6EoHWaGA2cC) | SS2020 | | 55. | **Probabilistic Machine Learning** | Philipp Hennig, Eberhard Karls Universität Tübingen | [Prob-ML](https://uni-tuebingen.de/en/180804) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij1tHaOFY96m5uX3J21a6yNd) | SS2020 | | 56. | **Machine Learning** | Sarath Chandar, PolyMTL, UdeM, Mila | [INF8953CE](http://sarathchandar.in/teaching/ml/fall2020) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLImtCgowF_ET0mi-AmmqQ0SIJUpWYaIOr) | F2020 | | 57. | **Machine Learning** | Erik Bekkers, Universiteit van Amsterdam | [UvA-ML](https://uvaml1.github.io/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL8FnQMH2k7jzhtVYbKmvrMyXDYMmgjj_n) | F2020 | | 58. | **Neural Networks for Signal Processing** | Shayan Srinivasa Garani, Indian Institute of Science | [NN4SP](https://labs.dese.iisc.ac.in/pnsil/neural-networks-and-learning-systems-i-fall-2020/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgMDNELGJ1CZn1399dV7_U4VBNJflRsua) | F2020 | | 59. | **Introduction to Machine Learning** | Dmitry Kobak, Universität Klinikum Tübingen | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij35ShKLDqccJSDntugY4FQT) | 2020 | | 60. | **Machine Learning (PRML)** | Erik J. Bekkers, Universiteit van Amsterdam | [UvAML-1](https://uvaml1.github.io) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL8FnQMH2k7jzhtVYbKmvrMyXDYMmgjj_n) | 2020 | | | | | | | | | 61. | **Machine Learning with Kernel Methods** | Julien Mairal and Jean-Philippe Vert, Inria/ENS Paris-Saclay, Google | [ML-Kernels](http://members.cbio.mines-paristech.fr/~jvert/svn/kernelcourse/course/2021mva/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLD93kGj6_EdrkNj27AZMecbRlQ1SMkp_o) | S2021 | | 62. | **Continual Learning** | Vincenzo Lomonaco, Università di Pisa | [ContLearn'21](https://course.continualai.org/background/details) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLm6QXeaB-XkBfM5RgQP6wCR7Jegdg51Px) | 2021 | | 63. | **Causality** | Christina Heinze-Deml, ETH Zurich | [Causal'21](https://stat.ethz.ch/lectures/ss21/causality.php#course_materials) | [YouTube-Lectures](https://stat.ethz.ch/lectures/ss21/causality.php#course_materials) | 2021 | | | | | | | | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ### :balloon: Reinforcement Learning :hotsprings: :video_game: :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: | S.No | Course Name | University/Instructor(s) | Course Webpage | Video Lectures | Year | | ---- | -------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------ | | 1. | **A Short Course on Reinforcement Learning** | Satinder Singh, UMichigan | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM4Pv4KYYzGy4cIFQ5C36-1jMNLab80Ky) | 2011 | | 2. | **Approximate Dynamic Programming** | Dimitri P. Bertsekas, MIT | [Lecture-Slides](http://adpthu2014.weebly.com/slides--materials.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLiCLbsFQNFAxOmVeqPhI5er1LGf2-L9I4) | 2014 | | 3. | **Introduction to Reinforcement Learning** | David Silver, DeepMind | [UCL-RL](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ) | 2015 | | 4. | **Reinforcement Learning** | Charles Isbell, Chris Pryby, GaTech; Michael Littman, Brown | [RL-Udacity](https://eu.udacity.com/course/reinforcement-learning--ud600) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLAwxTw4SYaPnidDwo9e2c7ixIsu_pdSNp) | 2015 | | 5. | **Reinforcement Learning** | Balaraman Ravindran, IIT Madras | [RL-IITM](https://www.cse.iitm.ac.in/~ravi/courses/Reinforcement%20Learning.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLNdWVHi37UggQIVcaZcmtGGEQHY9W7d9D) | 2016 | | 6. | **Deep Reinforcement Learning** | Sergey Levine, UC Berkeley | [CS-294](http://rail.eecs.berkeley.edu/deeprlcoursesp17/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLkFD6_40KJIwTmSbCv9OVJB3YaO4sFwkX) | S2017 | | 7. | **Deep Reinforcement Learning** | Sergey Levine, UC Berkeley | [CS-294](http://rail.eecs.berkeley.edu/deeprlcourse-fa17/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLkFD6_40KJIznC9CDbVTjAF2oyt8_VAe3) | F2017 | | 8. | **Deep RL Bootcamp** | Many legends, UC Berkeley | [Deep-RL](https://sites.google.com/view/deep-rl-bootcamp/lectures) | [YouTube-Lectures](https://www.youtube.com/channel/UCTgM-VlXKuylPrZ_YGAJHOw/videos) | 2017 | | 9 | **Data Efficient Reinforcement Learning** | Lots of Legends, Canary Islands | [DERL-17](http://dalimeeting.org/dali2017/data-efficient-reinforcement-learning.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL-tWvTpyd1VAvDpxukup6w-SuZQQ7e8K8) | 2017 | | 10. | **Deep Reinforcement Learning** | Sergey Levine, UC Berkeley | [CS-294-112](http://rail.eecs.berkeley.edu/deeprlcourse-fa18/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLkFD6_40KJIxJMR-j5A1mkxK26gh_qg37) | 2018 | | | | | | | | | 11. | **Reinforcement Learning** | Pascal Poupart, University of Waterloo | [CS-885](https://cs.uwaterloo.ca/~ppoupart/teaching/cs885-spring18/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdAoL1zKcqTXFJniO3Tqqn6xMBBL07EDc) | 2018 | | 12. | **Deep Reinforcement Learning and Control** | Katerina Fragkiadaki and Tom Mitchell, CMU | [10-703](http://www.andrew.cmu.edu/course/10-703/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLpIxOj-HnDsNfvOwRKLsUobmnF2J1l5oV) | 2018 | | 13. | **Reinforcement Learning and Optimal Control** | Dimitri Bertsekas, Arizona State University | [RLOC](http://web.mit.edu/dimitrib/www/RLbook.html) | [Lecture-Videos](http://web.mit.edu/dimitrib/www/RLbook.html) | 2019 | | 14. | **Reinforcement Learning** | Emma Brunskill, Stanford University | [CS 234](http://web.stanford.edu/class/cs234/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rOSOPzutgyCTapiGlY2Nd8u) | 2019 | | 15. | **Reinforcement Learning Day** | Lots of Legends, Microsoft Research, New York | [RLD-19](https://www.microsoft.com/en-us/research/event/reinforcement-learning-day-2019/#!agenda) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLD7HFcN7LXRe9nWEX3Up-RiCDi6-0mqVC) | 2019 | | 16. | **New Directions in Reinforcement Learning and Control** | Lots of Legends, IAS, Princeton University | [NDRLC-19](https://www.math.ias.edu/ndrlc) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdDZb3TwJPZ61sGqd6cbWCmTc275NrKu3) | 2019 | | 17. | **Deep Reinforcement Learning** | Sergey Levine, UC Berkeley | [CS 285](http://rail.eecs.berkeley.edu/deeprlcourse-fa19) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLkFD6_40KJIwhWJpGazJ9VSj9CFMkb79A) | F2019 | | 18. | **Deep Multi-Task and Meta Learning** | Chelsea Finn, Stanford University | [CS 330](https://cs330.stanford.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rMC6zfYmnD7UG3LVvwaITY5) | F2019 | | 19. | **RL-Theory Seminars** | Lots of Legends, Earth | [RL-theory-sem](https://sites.google.com/view/rltheoryseminars/past-seminars) | [YouTube-Lectures](https://www.youtube.com/channel/UCfBFutC9RbKK6p--B4R9ebA/videos) | 2020 - | | 20. | **Deep Reinforcement Learning** | Sergey Levine, UC Berkeley | [CS 285](http://rail.eecs.berkeley.edu/deeprlcourse-fa20) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_iWQOsE6TfURIIhCrlt-wj9ByIVpbfGc) | F2020 | | | | | | | | | 21. | **Introduction to Reinforcement Learning** | Amir-massoud Farahmand, Vector Institute, University of Toronto | [RL-intro](https://amfarahmand.github.io/IntroRL) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLCveiXxL2xNbiDq51a8iJwPRq2aO0ykrq) | S2021 | | 22. | **Reinforcement Learning** | Antonio Celani and Emanuele Panizon, International Centre for Theoretical Physics | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLp0hSY2uBeP8q2G3mfHGVGvQFEMX0QRWM) | 2021 | | 23. | **Computational Sensorimotor Learning** | Pulkit Agrawal, MIT-CSAIL | [6.884-CSL](https://pulkitag.github.io/6.884/lectures) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLwNwxAG-kBxPMTIs2fKWSsf7HqL2TcC78) | S2021 | | 24. | **Reinforcement Learning** | Dimitri P. Bertsekas, ASU/MIT | [RL-21](http://web.mit.edu/dimitrib/www/RLbook.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLmH30BG15SIp79JRJ-MVF12uvB1qPtPzn) | S2021 | | 25. | **Reinforcement Learning** | Sarath Chandar, École Polytechnique de Montréal | [INF8953DE](https://chandar-lab.github.io/INF8953DE) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLImtCgowF_ES_JdF_UcM60EXTcGZg67Ua) | F2021 | | 26. | **Deep Reinforcement Learning** | Sergey Levine, UC Berkeley | [CS 285](http://rail.eecs.berkeley.edu/deeprlcourse) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_iWQOsE6TfXxKgI1GgyV1B_Xa0DxE5eH) | F2021 | | 27. | **Reinforcement Learning Lecture Series** | Lots of Legends, DeepMind & UC London | [RL-series](https://deepmind.com/learning-resources/reinforcement-learning-series-2021) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm) | 2021 | | 28. | **Reinforcement Learning** | Dimitri P. Bertsekas, ASU/MIT | [RL-22](http://web.mit.edu/dimitrib/www/RLbook.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLmH30BG15SIoXhxLldoio0BhsIY84YMDj) | S2022 | | | | | | | | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ### :loudspeaker: Probabilistic Graphical Models :sparkles: :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: | S.No | Course Name | University/Instructor(s) | Course WebPage | Lecture Videos | Year | | ---- | ------------------------------------------------------------ | --------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------- | | 1. | **Probabilistic Graphical Models** | Many Legends, MPI-IS | [MLSS-Tuebingen](http://mlss.tuebingen.mpg.de/2013/2013/speakers.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLL0GjJzXhAWTRiW_ynFswMaiLSa0hjCZ3) | 2013 | | 2. | **Probabilistic Modeling and Machine Learning** | Zoubin Ghahramani, University of Cambridge | [WUST-Wroclaw](https://www.ii.pwr.edu.pl/~gonczarek/zoubin.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLwUOK5j_XOsdfVAGKErx9HqnrVZIuRbZ2) | 2013 | | 3. | **Probabilistic Graphical Models** | Eric Xing, CMU | [10-708](http://www.cs.cmu.edu/~epxing/Class/10708/lecture.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLI3nIOD-p5aoXrOzTd1P6CcLavu9rNtC-) | 2014 | | 4. | **Learning with Structured Data: An Introduction to Probabilistic Graphical Models** | Christoph Lampert, IST Austria | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLEqoHzpnmTfA0wc1JxjoVVOrJlx8W0rGf) | 2016 | | 5. | **Probabilistic Graphical Models** | Nicholas Zabaras, University of Notre Dame | [PGM](https://www.zabaras.com/probabilistic-graphical-models) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLd-PuDzW85AcV4bgdu7wHPL37hm60W4RM) | 2018 | | 6. | **Probabilistic Graphical Models** | Eric Xing, CMU | [10-708](https://sailinglab.github.io/pgm-spring-2019/) | [Lecture-Videos](https://sailinglab.github.io/pgm-spring-2019/lectures)
[YouTube-Lectures](https://www.youtube.com/playlist?list=PLoZgVqqHOumTY2CAQHL45tQp6kmDnDcqn) | S2019 | | 7. | **Probabilistic Graphical Models** | Eric Xing, CMU | [10-708](https://www.cs.cmu.edu/~epxing/Class/10708-20/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoZgVqqHOumTqxIhcdcpOAJOOimrRCGZn) | S2020 | | 8. | **Uncertainty Modeling in AI** | Gim Hee Lee, National University of Singapura (NUS) | [CS 5340 - CH](https://www.coursehero.com/sitemap/schools/2652-National-University-of-Singapore/courses/7821096-CS5340/), [CS 5340-NB](https://github.com/clear-nus/CS5340-notebooks) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLxg0CGqViygOb9Eyc8IXM27doxjp2SK0H) | 2020-21 | | | | | | | | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ## :game_die: Bayesian Deep Learning :spades: :gem: :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: | S.No | Course Name | University/Instructor(s) | Course WebPage | Lecture Videos | Year | | ---- | --------------------------------------------------- | --------------------------------- | -------------------------------------------------------- | ------------------------------------------------------------ | -------- | | 1. | **Bayesian Neural Networks, Variational Inference** | Lots of Legends | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM4Pv4KYYzGwUB4bFy183hwGhpL9ytvA1) | 2014-now | | 2. | **Variational Inference** | Chieh Wu, Northeastern University | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdk2fd27CQzSd1sQ3kBYL4vtv6GjXvPsE) | 2015 | | 3. | **Deep Learning and Bayesian Methods** | Lots of Legends, HSE Moscow | [DLBM-SS](http://deepbayes.ru/2018) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLe5rNUydzV9Q01vWCP9BV7NhJG3j7mz62) | 2018 | | 4. | **Deep Learning and Bayesian Methods** | Lots of Legends, HSE Moscow | [DLBM-SS](http://deepbayes.ru/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLe5rNUydzV9QHe8VDStpU0o8Yp63OecdW) | 2019 | | 5. | **Nordic Probabilistic AI** | Lots of Legends, NTNU, Trondheim | [ProbAI](https://github.com/probabilisticai/probai-2019) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLRy-VW__9hV8s--JkHXZvnd26KgjRP2ik) | 2019 | | | | | | | | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ## :movie_camera: Medical Imaging :camera: :video_camera: :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: | S.No | Course Name | University/Instructor(s) | Course WebPage | Lecture Videos | Year | | ---- | ------------------------------------------------------------ | ------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ----- | | 1. | **Medical Imaging Summer School** | Lots of Legends, Sicily | [MISS-14](http://iplab.dmi.unict.it/miss14/programme.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_VeUGLULXQtvcCdAgmvKoJ1k0Ajhz-Qu) | 2014 | | 2. | **Biomedical Image Analysis Summer School** | Lots of Legends, Paris | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgSHH6boFf5uJAUT4ZRiAZc_ofXolkAGK) | 2015 | | 3. | **Medical Imaging Summer School** | Lots of Legends, Sicily | [MISS-16](http://iplab.dmi.unict.it/miss16/programme.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTRCr47yTx5iXIYSneX3LKf16upaw59wa) | 2016 | | 4. | **OPtical and UltraSound imaging - OPUS** | Lots of Legends, Université de Lyon, France | [OPUS'16](https://opus2016lyon.sciencesconf.org/resource/page/id/2) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL95ayoVLX8GdUKbxu-R9WqRWwzdWcKjti) | 2016 | | 5. | **Medical Imaging Summer School** | Lots of Legends, Sicily | [MISS-18](http://iplab.dmi.unict.it/miss/programme.htm) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_VeUGLULXQux1dV4iA3XuMX6AueJmGGa) | 2018 | | 6. | **Seminar on AI in Healthcare** | Lots of Legends, Stanford | [CS 522](http://cs522.stanford.edu/2018/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLYn-ZmPR1DtNQJ-ot-L2V2EgUEH6OH_7w) | 2018 | | 7. | **Machine Learning for Healthcare** | David Sontag, Peter Szolovits, CSAIL MIT | [MLHC-19](https://mlhc19mit.github.io/)
[MIT 6.S897](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-s897-machine-learning-for-healthcare-spring-2019/lecture-notes/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLUl4u3cNGP60B0PQXVQyGNdCyCTDU1Q5j) | S2019 | | 8. | **Deep Learning and Medical Applications** | Lots of Legends, IPAM, UCLA | [DLM-20](https://www.ipam.ucla.edu/programs/workshops/deep-learning-and-medical-applications/?tab=schedule) | [Lecture-Videos](https://www.ipam.ucla.edu/programs/workshops/deep-learning-and-medical-applications/?tab=schedule) | 2020 | | 9. | **Stanford Symposium on Artificial Intelligence in Medicine and Imaging** | Lots of Legends, Stanford AIMI | [AIMI-20](https://aimi.stanford.edu/news-events/aimi-symposium/agenda) | [YouTube-Lectures](https://www.youtube.com/watch?v=tR2ObiL4il8&list=PLe6zdIMe5B7IR0oDOobXBDBlYY1eqLYPx) | 2020 | | | | | | | | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ## :tada: Graph Neural Networks (Geometric DL) :confetti_ball: :balloon: :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: | S.No | Course Name | University/Instructor(s) | Course WebPage | Lecture Videos | Year | | ---- | ------------------------------------------------------------ | ------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ----- | | 1. | **Deep learning on graphs and manifolds** | Michael Bronstein, Technion | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLH39kM3nuavcVOUIIBraBNHjv-CwEd1uV) | 2017 | | 2. | **Geometric Deep Learning on Graphs and Manifolds** | Michael Bronstein, Technische Universität München | `None` | [Lec-part1](https://streams.tum.de/Mediasite/Play/1f3b894e78f6400daa7885c886b936fb1d),
[Lec-part2](https://streams.tum.de/Mediasite/Play/6039c846b2f84e7a806024c06e3f5c5c1d) | 2017 | | 3. | **Eurographics Symposium on Geometry Processing - Graduate School** | Lots of Legends, SIGGRAPH, London | [SGP-2017](http://geometry.cs.ucl.ac.uk/SGP2017/?p=gradschool) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLOp-ngXvomHArqntgLVNzuJNdzNx3rDjZ) | 2017 | | 4. | **Eurographics Symposium on Geometry Processing - Graduate School** | Lots of Legends, SIGGRAPH, Paris | [SGP-2018](https://sgp2018.sciencesconf.org/resource/page/id/7) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLvcoRb-DvAmgpp8LYw7dUvLxh-1Vrrm-v) | 2018 | | 5. | **Analysis of Networks: Mining and Learning with Graphs** | Jure Leskovec, Stanford University | [CS224W](http://snap.stanford.edu/class/cs224w-2018/) | [Lecture-Videos](http://snap.stanford.edu/class/cs224w-2018/) | 2018 | | 6. | **Machine Learning with Graphs** | Jure Leskovec, Stanford University | [CS224W](http://snap.stanford.edu/class/cs224w-2019/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL-Y8zK4dwCrQyASidb2mjj_itW2-YYx6-) | 2019 | | 7. | Geometry and Learning from Data in 3D and Beyond -**Geometry and Learning from Data Tutorials** | Lots of Legends, IPAM UCLA | [GLDT](http://www.ipam.ucla.edu/programs/workshops/geometry-and-learning-from-data-tutorials) | [Lecture-Videos](http://www.ipam.ucla.edu/programs/workshops/geometry-and-learning-from-data-tutorials/?tab=schedule) | 2019 | | 8. | Geometry and Learning from Data in 3D and Beyond - **Geometric Processing** | Lots of Legends, IPAM UCLA | [GeoPro](http://www.ipam.ucla.edu/programs/workshops/workshop-i-geometric-processing/) | [Lecture-Videos](http://www.ipam.ucla.edu/programs/workshops/workshop-i-geometric-processing/?tab=schedule) | 2019 | | 9. | Geometry and Learning from Data in 3D and Beyond - **Shape Analysis** | Lots of Legends, IPAM UCLA | [Shape-Analysis](http://www.ipam.ucla.edu/programs/workshops/workshop-ii-shape-analysis/) | [Lecture-Videos](http://www.ipam.ucla.edu/programs/workshops/workshop-ii-shape-analysis/?tab=schedule) | 2019 | | 10. | Geometry and Learning from Data in 3D and Beyond - **Geometry of Big Data** | Lots of Legends, IPAM UCLA | [Geo-BData](http://www.ipam.ucla.edu/programs/workshops/workshop-iii-geometry-of-big-data) | [Lecture-Videos](http://www.ipam.ucla.edu/programs/workshops/workshop-iii-geometry-of-big-data/?tab=schedule) | 2019 | | | | | | | | | 11. | Geometry and Learning from Data in 3D and Beyond - **Deep Geometric Learning of Big Data and Applications** | Lots of Legends, IPAM UCLA | [DGL-BData](http://www.ipam.ucla.edu/programs/workshops/workshop-iv-deep-geometric-learning-of-big-data-and-applications) | [Lecture-Videos](http://www.ipam.ucla.edu/programs/workshops/workshop-iv-deep-geometric-learning-of-big-data-and-applications/?tab=schedule) | 2019 | | 12. | **Israeli Geometric Deep Learning** | Lots of Legends, Israel | [iGDL-20](https://gdl-israel.github.io/schedule.html) | [Lecture-Videos](https://www.youtube.com/watch?v=c8_32IVn-sg) | 2020 | | 13. | **Machine Learning for Graphs and Sequential Data** | Stephan Günnemann, Technische Universität München (TUM) | [MLGS-20](https://www.in.tum.de/en/daml/teaching/summer-term-2020/machine-learning-for-graphs-and-sequential-data/) | [Lecture-Videos](https://www.in.tum.de/daml/teaching/mlgs/) | S2020 | | 14. | **Machine Learning with Graphs** | Jure Leskovec, Stanford | [CS224W](http://web.stanford.edu/class/cs224w) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rPLKxIpqhjhPgdQy7imNkDn) | W2021 | | 15. | **Geometric Deep Learning** - AMMI | Lots of Legends, Virtual | [GDL-AMMI](https://geometricdeeplearning.com/lectures) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLn2-dEmQeTfQ8YVuHBOvAhUlnIPYxkeu3) | 2021 | | 16. | **Summer School on Geometric Deep Learning** - | Lots of Legends, DTU, DIKU & AAU | [GDL- DTU, DIKU & AAU](https://geometric-deep-learning.compute.dtu.dk) | [Lecture-Videos](https://geometric-deep-learning.compute.dtu.dk/talks-and-materials) | 2021 | | 17. | **Graph Neural Networks** | Alejandro Ribeiro, University of Pennsylvania | [ESE 514](https://gnn.seas.upenn.edu) | [YouTube-Lectures](https://www.youtube.com/channel/UC_YPrqpiEqkeGOG1TCt0giQ/playlists) | F2021 | | | | | | | | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ### :hibiscus: Natural Language Processing :cherry_blossom: :sparkling_heart: :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: | S.No | Course Name | University/Instructor(s) | Course WebPage | Lecture Videos | Year | | ---- | --------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | --------- | | 1. | **Computational Linguistics I** | Jordan Boyd-Graber, University of Maryland | [CMS-723](http://users.umiacs.umd.edu/~jbg/teaching/CMSC_723/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLegWUnz91WfuPebLI97-WueAP90JO-15i) | 2013-2018 | | 2. | **Deep Learning for Natural Language Processing** | Nils Reimers, TU Darmstadt | [DL4NLP](https://github.com/UKPLab/deeplearning4nlp-tutorial) | [YouTube-Lectures](https://www.youtube.com/channel/UC1zCuTrfpjT6Sv2kJk-JkvA/videos) | 2015-2017 | | 3. | **Deep Learning for Natural Language Processing** | Many Legends, DeepMind-Oxford | [DL-NLP](http://www.cs.ox.ac.uk/teaching/courses/2016-2017/dl/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL613dYIGMXoZBtZhbyiBqb0QtgK6oJbpm) | 2017 | | 4. | **Deep Learning for Speech & Language** | UPC Barcelona | [DL-SL](https://telecombcn-dl.github.io/2017-dlsl/) | [Lecture-Videos](https://telecombcn-dl.github.io/2017-dlsl/) | 2017 | | 5. | **Neural Networks for Natural Language Processing** | Graham Neubig, CMU | [NN4NLP](http://www.phontron.com/class/nn4nlp2017/) [Code](https://github.com/neubig/nn4nlp-code) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL8PYTP1V4I8ABXzdqtOpB_eqBlVAz_xPT) | 2017 | | 6. | **Neural Networks for Natural Language Processing** | Graham Neubig, CMU | [NN4-NLP](http://www.phontron.com/class/nn4nlp2018/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL8PYTP1V4I8Ba7-rY4FoB4-jfuJ7VDKEE) | 2018 | | 7. | **Deep Learning for NLP** | Min-Yen Kan, NUS | [CS-6101](https://www.comp.nus.edu.sg/~kanmy/courses/6101_1810/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLllwxvcS7ca5eD44KTCiT7Rmu_hFAafXB) | 2018 | | 8. | **Neural Networks for Natural Language Processing** | Graham Neubig, CMU | [NN4NLP](http://www.phontron.com/class/nn4nlp2019/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL8PYTP1V4I8Ajj7sY6sdtmjgkt7eo2VMs) | 2019 | | 9. | **Natural Language Processing with Deep Learning** | Abigail See, Chris Manning, Richard Socher, Stanford University | [CS224n](http://web.stanford.edu/class/cs224n/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z) | 2019 | | 10. | **Natural Language Understanding** | Bill MacCartney and Christopher Potts | [CS224U](https://web.stanford.edu/class/cs224u) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rObpMCir6rNNUlFAn56Js20) | S2019 | | | | | | | | | 11. | **Neural Networks for Natural Language Processing** | Graham Neubig, Carnegie Mellon University | [CS 11-747](http://www.phontron.com/class/nn4nlp2020/schedule.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL8PYTP1V4I8CJ7nMxMC8aXv8WqKYwj-aJ) | S2020 | | 12. | **Advanced Natural Language Processing** | Mohit Iyyer, UMass Amherst | [CS 685](https://people.cs.umass.edu/~miyyer/cs685) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLWnsVgP6CzadmQX6qevbar3_vDBioWHJL) | F2020 | | 13. | **Machine Translation** | Philipp Koehn, Johns Hopkins University | [EN 601.468/668](http://mt-class.org/jhu/syllabus.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLQrCiUDqDLG0lQX54o9jB4phJ-SLI6ZBQ) | F2020 | | 14. | **Neural Networks for NLP** | Graham Neubig, Carnegie Mellon University | [CS 11-747](http://www.phontron.com/class/nn4nlp2021) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL8PYTP1V4I8AkaHEJ7lOOrlex-pcxS-XV) | 2021 | | 15. | **Deep Learning for Natural Language Processing** | Kyunghyun Cho, New York University | [DS-GA 1011](https://drive.google.com/drive/folders/1ykXBtophaY_65VHK_8yDzZQJwfJDD5Ve) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdH9u0f1XKW_s-c8EcgJpn_HJz5Jj1IRf) | F2021 | | 16. | **Natural Language Processing with Deep Learning** | Chris Manning, Stanford University | [CS224n](https://web.stanford.edu/class/archive/cs/cs224n/cs224n.1214/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rOSH4v6133s9LFPRHjEmbmJ) | 2021 | | | | | | | | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ### :speaking_head: Automatic Speech Recognition :speech_balloon: :thought_balloon: :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: | S.No | Course Name | University/Instructor(s) | Course WebPage | Lecture Videos | Year | | ---- | ---------------------------------------- | ------------------------------ | --------------------------------------------------- | ------------------------------------------------------------ | --------- | | 1. | **Deep Learning for Speech & Language** | UPC Barcelona | [DL-SL](https://telecombcn-dl.github.io/2017-dlsl/) | [Lecture-Videos](https://telecombcn-dl.github.io/2017-dlsl/)
[YouTube-Videos](https://www.youtube.com/playlist?list=PL-5DCZHuHZkWeF9ljIjoC_X5gHRLNtIkU) | 2017 | | 2. | **Speech and Audio in the Northeast** | Many Legends, Google NYC | [SANE-15](http://www.saneworkshop.org/sane2015/) | [YouTube-Videos](https://www.youtube.com/playlist?list=PLBJWRPcgwk7sZOB4UTVilWWnRg84L9o5i) | 2015 | | 3. | **Automatic Speech Recognition** | Samudra Vijaya K, TIFR | `None` | [YouTube-Videos](https://www.youtube.com/channel/UCHk6uq1Cr9J3k5KNmIsYUNw/videos) | 2016 | | 4. | **Speech and Audio in the Northeast** | Many Legends, Google NYC | [SANE-17](http://www.saneworkshop.org/sane2017/) | [YouTube-Videos](https://www.youtube.com/playlist?list=PLBJWRPcgwk7tNLaBVu_S90ZQSblO3bwjg) | 2017 | | 5. | **Speech and Audio in the Northeast** | Many Legends, Google Cambridge | [SANE-18](http://www.saneworkshop.org/sane2018/) | [YouTube-Videos](https://www.youtube.com/playlist?list=PLBJWRPcgwk7sjMANn8jqosyHIMe6DJhmn) | 2018 | | | | | | | | | -1. | **Deep Learning for Speech Recognition** | Many Legends, AoE | `None` | [YouTube-Videos](https://www.youtube.com/playlist?list=PLM4Pv4KYYzGyFYCXV6YPWAKVOR2gmHnQd) | 2015-2018 | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ### :fire: Modern Computer Vision :camera_flash: :movie_camera: :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: | S.No | Course Name | University/Instructor(s) | Course WebPage | Lecture Videos | Year | | ---- | ------------------------------------------------------------ | ------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ---------- | | 1. | **Microsoft Computer Vision Summer School** - (classical) | Lots of Legends, Lomonosov Moscow State University | `None` | [YouTube-Videos](https://www.youtube.com/playlist?list=PLbwKcm5vdiSYU54xFUG1zoxQTulqvIcJu)
[Russian-mirror](https://www.youtube.com/playlist?list=PL-_cKNuVAYAUp0eCL7KO8QY4ETY3tIDFH) | 2011 | | 2. | **Computer Vision** - (classical) | Mubarak Shah, UCF | [CAP-5415](http://crcv.ucf.edu/courses/CAP5415/Fall2012/index.php) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLd3hlSJsX_Imk_BPmB_H3AQjFKZS9XgZm) | 2012 | | 3. | **Image and Multidimensional Signal Processing** - (classical) | William Hoff, Colorado School of Mines | [CSCI 510/EENG 510](http://inside.mines.edu/~whoff/courses/EENG510) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLyED3W677ALNv8Htn0f9Xh-AHe1aZPftv) | 2012 | | 4. | **Computer Vision** - (classical) | William Hoff, Colorado School of Mines | [CSCI 512/EENG 512](http://inside.mines.edu/~whoff/courses/EENG512/index.htm) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL4B3F8D4A5CAD8DA3) | 2012 | | 5. | **Image and Video Processing: From Mars to Hollywood with a Stop at the Hospital** | Guillermo Sapiro, Duke University | `None` | [YouTube-Videos](https://www.youtube.com/playlist?list=PLZ9qNFMHZ-A79y1StvUUqgyL-O0fZh2rs) | 2013 | | 6. | **Multiple View Geometry** (classical) | Daniel Cremers, Technische Universität München | [mvg](https://vision.in.tum.de/teaching/ss2014/mvg2014) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTBdjV_4f-EJn6udZ34tht9EVIW7lbeo4) | 2013 | | 7. | **Mathematical Methods for Robotics, Vision, and Graphics** | Justin Solomon, Stanford University | [CS-205A](http://graphics.stanford.edu/courses/cs205a/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLQ3UicqQtfNvQ_VzflHYKhAqZiTxOkSwi) | 2013 | | 8. | **Computer Vision** - (classical) | Mubarak Shah, UCF | [CAP-5415](http://crcv.ucf.edu/courses/CAP5415/Fall2014/index.php) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLd3hlSJsX_ImKP68wfKZJVIPTd8Ie5u-9) | 2014 | | 9. | **Computer Vision for Visual Effects** (classical) | Rich Radke, Rensselaer Polytechnic Institute | [ECSE-6969](https://www.ecse.rpi.edu/~rjradke/cvfxcourse.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLuh62Q4Sv7BUJlKlt84HFqSWfW36MDd5a) | S2014 | | 10. | **Autonomous Navigation for Flying Robots** | Juergen Sturm, Technische Universität München | [Autonavx](https://jsturm.de/wp/teaching/autonavx-slides/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTBdjV_4f-EKBCUs1HmMtsnXv4JUoFrzg) | 2014 | | | | | | | | | 11. | **SLAM - Mobile Robotics** | Cyrill Stachniss, Universitaet Freiburg | [RobotMapping](http://ais.informatik.uni-freiburg.de/teaching/ws13/mapping/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgnQpQtFTOGQrZ4O5QzbIHgl3b1JHimN_) | 2014 | | 12. | **Computational Photography** | Irfan Essa, David Joyner, Arpan Chakraborty | [CP-Udacity](https://eu.udacity.com/course/computational-photography--ud955) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLAwxTw4SYaPn-unAWtRMleY4peSe4OzIY) | 2015 | | 13. | **Introduction to Digital Image Processing** | Rich Radke, Rensselaer Polytechnic Institute | [ECSE-4540](https://www.ecse.rpi.edu/~rjradke/improccourse.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLuh62Q4Sv7BUf60vkjePfcOQc8sHxmnDX) | S2015 | | 14. | **Lectures on Digital Photography** | Marc Levoy, Stanford/Google Research | [LoDP](https://sites.google.com/site/marclevoylectures/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL7ddpXYvFXspUN0N-gObF1GXoCA-DA-7i) | 2016 | | 15. | **Introduction to Computer Vision** (foundation) | Aaron Bobick, Irfan Essa, Arpan Chakraborty | [CV-Udacity](https://eu.udacity.com/course/introduction-to-computer-vision--ud810) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLAwxTw4SYaPnbDacyrK_kB_RUkuxQBlCm) | 2016 | | 16. | **Computer Vision** | Syed Afaq Ali Shah, University of Western Australia | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLvqB6_mDBCdlnT84LK_NvbOqcXLlOTR8j) | 2016 | | 17. | **Photogrammetry I & II** | Cyrill Stachniss, University of Bonn | [PG-I&II](https://www.ipb.uni-bonn.de/photogrammetry-i-ii/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgnQpQtFTOGRsi5vzy9PiQpNWHjq-bKN1) | 2016 | | 18. | **Deep Learning for Computer Vision** | UPC Barcelona | [DLCV-16](http://imatge-upc.github.io/telecombcn-2016-dlcv/)
[DLCV-17](https://telecombcn-dl.github.io/2017-dlcv/)
[DLCV-18](https://telecombcn-dl.github.io/2018-dlcv/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL-5eMc3HQTBbuaTFP4wsfD2Y2VqEfQcaP) | 2016-2018 | | 19. | **Convolutional Neural Networks** | Andrew Ng, Stanford University | [DeepLearning.AI](https://www.deeplearning.ai/deep-learning-specialization/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLkDaE6sCZn6Gl29AoE31iwdVwSG-KnDzF) | 2017 | | 20. | **Variational Methods for Computer Vision** | Daniel Cremers, Technische Universität München | [VMCV](https://vision.in.tum.de/teaching/ws2016/vmcv2016) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTBdjV_4f-EJ7A2iIH5L5ztqqrWYjP2RI) | 2017 | | | | | | | | | 21. | **Winter School on Computer Vision** | Lots of Legends, Israel Institute for Advanced Studies | [WS-CV](http://www.as.huji.ac.il/cse) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTn74Qx5mPsSniA5tt6W-o0OGYEeKScug) | 2017 | | 22. | **Deep Learning for Visual Computing** | Debdoot Sheet, IIT-Kgp | [Nptel](https://onlinecourses.nptel.ac.in/noc18_ee08/preview) [Notebooks](https://github.com/iitkliv/dlvcnptel) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLuv3GM6-gsE1Biyakccxb3FAn4wBLyfWf) | 2018 | | 23. | **The Ancient Secrets of Computer Vision** | Joseph Redmon, Ali Farhadi | [TASCV](https://pjreddie.com/courses/computer-vision/) ; [TASCV-UW](https://courses.cs.washington.edu/courses/cse455/18sp/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLjMXczUzEYcHvw5YYSU92WrY8IwhTuq7p) | 2018 | | 24. | **Modern Robotics** | Kevin Lynch, Northwestern Robotics | [modern-robot](http://hades.mech.northwestern.edu/index.php/Modern_Robotics) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLggLP4f-rq02vX0OQQ5vrCxbJrzamYDfx) | 2018 | | 25. | **Digial Image Processing** | Alex Bronstein, Technion | [CS236860](https://vistalab-technion.github.io/cs236860/info/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM0a6Z788YAZOxUyWda9y3N_i2upIj1Ep) | 2018 | | 26. | **Mathematics of Imaging** - Variational Methods and Optimization in Imaging | Lots of Legends, Institut Henri Poincaré | [Workshop-1](http://www.ihp.fr/sites/default/files/conf1-04_au_08_fevr-imaging2019.pdf) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL9kd4mpdvWcAzD5Aq-P1TrLLiYckrloxw) | 2019 | | 27. | **Deep Learning for Video** | Xavier Giró, UPC Barcelona | [deepvideo](https://mcv-m6-video.github.io/deepvideo-2019/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL-5eMc3HQTBbPY-627Gornj09pZrNQgPD) | 2019 | | 28. | **Statistical modeling for shapes and imaging** | Lots of Legends, Institut Henri Poincaré, Paris | [workshop-2](https://imaging-in-paris.github.io/semester2019/workshop2prog) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL9kd4mpdvWcAzD5Aq-P1TrLLiYckrloxw) | 2019 | | 29. | **Imaging and machine learning** | Lots of Legends, Institut Henri Poincaré, Paris | [workshop-3](https://imaging-in-paris.github.io/semester2019/workshop3prog) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL9kd4mpdvWcAzD5Aq-P1TrLLiYckrloxw) | 2019 | | 30. | **Computer Vision** | Jayanta Mukhopadhyay, IIT Kgp | [CV-nptel](https://nptel.ac.in/courses/106/105/106105216/) | [YouTube-Lectures](https://nptel.ac.in/courses/106/105/106105216/) | 2019 | | | | | | | | | 31. | **Deep Learning for Computer Vision** | Justin Johnson, UMichigan | [EECS 498-007](https://web.eecs.umich.edu/~justincj/teaching/eecs498/) | [Lecture-Videos](http://leccap.engin.umich.edu/leccap/site/jhygcph151x25gjj1f0)
[YouTube-Lectures](https://www.youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r) | 2019 | | 32. | **Sensors and State Estimation 2** | Cyrill Stachniss, University of Bonn | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgnQpQtFTOGQh_J16IMwDlji18SWQ2PZ6) | S2020 | | 33. | **Computer Vision III: Detection, Segmentation and Tracking** | Laura Leal-Taixé, TU München | [CV3DST](https://dvl.in.tum.de/teaching/cv3dst-ss20/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLog3nOPCjKBneGyffEktlXXMfv1OtKmCs) | S2020 | | 34. | **Advanced Deep Learning for Computer Vision** | Laura Leal-Taixé and Matthias Nießner, TU München | [ADL4CV](https://dvl.in.tum.de/teaching/adl4cv-ss20) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLog3nOPCjKBnjhuHMIXu4ISE4Z4f2jm39) | S2020 | | 35. | **Computer Vision: Foundations** | Fred Hamprecht, Universität Heidelberg | [CVF](https://hci.iwr.uni-heidelberg.de/ial/cvf) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLuRaSnb3n4kRAbnmiyGd77hyoGzO9wPde) | SS2020 | | 36. | **MIT Vision Seminar** | Lots of Legends, MIT | [MIT-Vision](https://sites.google.com/view/visionseminar/past-talks) | [YouTube-Lectures](https://www.youtube.com/channel/UCLMiFkFyfcNnZs6iwYLPI9g/videos) | 2015-now | | 37. | **TUM AI Guest Lectures** | Lots of Legends, Technische Universität München | [TUM-AI](https://niessner.github.io/TUM-AI-Lecture-Series) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLQ8Y4kIIbzy8kMlz7cRqz-BjbdyWsfLXt) | 2020 - now | | 38. | **Seminar on 3D Geometry & Vision** | Lots of Legends, Virtual | [3DGV seminar](https://3dgv.github.io) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZk0jtN0g8e-xVTfsiV67q8Iz1cZO_FpV) | 2020 - now | | 39. | **Event-based Robot Vision** | Guillermo Gallego, Technische Universität Berlin | [EVIS-SS20](https://sites.google.com/view/guillermogallego/teaching/event-based-robot-vision) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL03Gm3nZjVgUFYUh3v5x8jVonjrGfcal8) | 2020 - now | | 40. | **Deep Learning for Computer Vision** | Vineeth Balasubramanian, IIT Hyderabad | [DL-CV'20](https://onlinecourses.nptel.ac.in/noc20_cs88/preview) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLyqSpQzTE6M_PI-rIz4O1jEgffhJU9GgG) | 2020 | | | | | | | | | 41. | **Deep Learning for Visual Computing** | Peter Wonka, KAUST, SA | `NOne` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLMpQLEui13s2DHbw6kTTxwQma8rehlfZE) | 2020 | | 42. | **Computer Vision** | Yogesh Rawat, University of Central Florida | [CAP5415-CV](https://www.crcv.ucf.edu/courses/cap5415-fall-2020/schedule/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLd3hlSJsX_Ikm5il1HgmDB_z62BeoikFX) | F2020 | | 43. | **Multimedia Signal Processing** | Mark Hasegawa-Johnson, UIUC | [ECE-417 MSP](https://courses.engr.illinois.edu/ece417/fa2020/) | [Lecture Videos](https://mediaspace.illinois.edu/channel/ECE%20417/26816181) | F2020 | | 44. | **Computer Vision** | Andreas Geiger, Universität Tübingen | [Comp.Vis](https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/autonomous-vision/lectures/computer-vision/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij35L2MHGzis8AEHz7mg381_) | S2021 | | 45. | **3D Computer Vision** | Lee Gim Hee, National Univeristy of Singapura | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLxg0CGqViygP47ERvqHw_v7FVnUovJeaz) | 2021 | | 46. | **Deep Learning for Computer Vision: Fundamentals and Applications** | T. Dekel et al., Weizmann Institute of Science | [DL4CV](https://dl4cv.github.io/schedule.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_Z2_U9MIJdNgFM7-f2fZ9ZxjVRP_jhJv) | S2021 | | 47. | **Current Topics in ML Methods in 3D and Geometric Deep Learning** | Animesh Garg & others, University of Toronto | [CSC 2547](http://www.pair.toronto.edu/csc2547-w21) | [YouTube-Lectures](https://www.youtube.com/channel/UCrsmAXnwu6sgccWevW12Dfg/videos) | 2021 | | 48. | **First Principles of Computer Vision** | Shree K. Nayar, Columbia University | [FPCV](https://fpcv.cs.columbia.edu) | [YouTube-Lectures](https://www.youtube.com/channel/UCf0WB91t8Ky6AuYcQV0CcLw/videos) | 2021 | | 49. | **Self-Driving Cars** | Andreas Geiger, Universität Tübingen | [SDC'21](https://uni-tuebingen.de/de/123611) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij321zzKXK6XCQXAaaYjQbzr) | W2021 | | | | | | | | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ### :star2: Boot Camps or Summer Schools :maple_leaf: :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: | S.No | Course Name | University/Instructor(s) | Course WebPage | Lecture Videos | Year | | ---- | ------------------------------------------------------- | -------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | --------- | | 1. | **Deep Learning, Feature Learning** | Lots of Legends, IPAM UCLA | [GSS-2012](https://www.ipam.ucla.edu/programs/summer-schools/graduate-summer-school-deep-learning-feature-learning/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLHyI3Fbmv0SdzMHAy0aN59oYnLy5vyyTA) | 2012 | | 2. | **Big Data Boot Camp** | Lots of Legends, Simons Institute | [Big Data](https://simons.berkeley.edu/workshops/schedule/316) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre13RmUC2AybRvVAxO5DEMIBH) | 2013 | | 3. | **Machine Learning Summer School** | Lots of Legends, MPI-IS Tübingen | [MLSS-13](http://mlss.tuebingen.mpg.de/2013/2013/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLqJm7Rc5-EXFv6RXaPZzzlzo93Hl0v91E) | 2013 | | 4 | **Graduate Summer School: Computer Vision** | Lots of Legends, IPAM-UCLA | [GSS-CV](http://www.ipam.ucla.edu/programs/summer-schools/graduate-summer-school-computer-vision/) | [Video-Lectures](http://www.ipam.ucla.edu/programs/summer-schools/graduate-summer-school-computer-vision/?tab=schedule) | 2013 | | 5. | **Machine Learning Summer School** | Lots of Legends, Reykjavik University | [MLSS-14](http://mlss2014.hiit.fi/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLqdbxUnkqOw2nKn7VxYqIrKWcqRkQYOsF) | 2014 | | 6. | **Machine Learning Summer School** | Lots of Legends, Pittsburgh | [MLSS-14](http://www.mlss2014.com) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZSO_6-bSqHQCIYxE3ycGLXHMjK3XV7Iz) | 2014 | | 7. | **Deep Learning Summer School** | Lots of Legends, Université de Montréal | [DLSS-15](https://sites.google.com/site/deeplearningsummerschool/home) | [YouTube-Lectures](http://videolectures.net/deeplearning2015_montreal/) | 2015 | | 8. | **Biomedical Image Analysis Summer School** | Lots of Legends, CentraleSupelec, Paris | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgSHH6boFf5uJAUT4ZRiAZc_ofXolkAGK) | 2015 | | 9. | **Mathematics of Signal Processing** | Lots of Legends, Hausdorff Institute for Mathematics | [SigProc](http://www.him.uni-bonn.de/signal-processing-2016/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLul8LCT3AJqSQo3lr5RbwxJ92RsgRuDtx) | 2016 | | 10. | **Microsoft Research - Machine Learning Course** | S V N Vishwanathan and Prateek Jain MS-Research | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL34iyE0uXtxo7vPXGFkmm6KbgZQwjf9Kf) | 2016 | | | | | | | | | 11. | **Deep Learning Summer School** | Lots of Legends, Université de Montréal | [DL-SS-16](https://sites.google.com/site/deeplearningsummerschool2016/home) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL5bqIc6XopCbb-FvnHmD1neVlQKwGzQyR) | 2016 | | 12. | **Lisbon Machine Learning School** | Lots of Legends, Instituto Superior Técnico, Portugal | [LxMLS-16](http://lxmls.it.pt/2016/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLToLj8M4ao-fymxXBIOU6sF1NGFLb5EiX) | 2016 | | 13. | **Machine Learning Advances and Applications Seminar** | Lots of Legends, Fields Institute, University of Toronto | [MLAAS-16](http://www.fields.utoronto.ca/activities/16-17/machine-learning) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLfsVAYSMwskuQcRkuDApP40lX_i08d0QK)
[Video-Lectures](http://www.fields.utoronto.ca/video-archive/event/2267) | 2016-2017 | | 14. | **Machine Learning Advances and Applications Seminar** | Lots of Legends, Fields Institute, University of Toronto | [MLAAS-17](http://www.fields.utoronto.ca/activities/17-18/machine-learning) | [Video Lectures](http://www.fields.utoronto.ca/video-archive/event/2487) | 2017-2018 | | 15. | **Machine Learning Summer School** | Lots of Legends, MPI-IS Tübingen | [MLSS-17](http://mlss.tuebingen.mpg.de/2017/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLqJm7Rc5-EXFUOvoYCdKikfck8YeUCnl9) | 2017 | | 16. | **Representation Learning** | Lots of Legends, Simons Institute | [RepLearn](https://simons.berkeley.edu/workshops/abstracts/3750) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre13UNV4ztsWUXciUZ7x_ZDHz) | 2017 | | 17. | **Foundations of Machine Learning** | Lots of Legends, Simons Institute | [ML-BootCamp](https://simons.berkeley.edu/workshops/abstracts/3748) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre11GbZWneln-VZDLHyejO7YD) | 2017 | | 18. | **Optimization, Statistics, and Uncertainty** | Lots of Legends, Simons Institute | [Optim-Stats](https://simons.berkeley.edu/workshops/abstracts/4795) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre13ACD44z2FH-IVP1e8ip5JO) | 2017 | | 19. | **Deep Learning: Theory, Algorithms, and Applications** | Lots of Legends, TU-Berlin | [DL: TAA](http://doc.ml.tu-berlin.de/dlworkshop2017/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLJOzdkh8T5kqCNV_v1w2tapvtJDZYiohW) | 2017 | | 20. | **Deep Learning and Reinforcement Learning Summer School** | Lots of Legends, Université de Montréal | [DLRL-2017](https://mila.quebec/en/cours/deep-learning-summer-school-2017/) | [Lecture-videos](http://videolectures.net/deeplearning2017_montreal/) | 2017 | | | | | | | | | 21. | **Statistical Physics Methods in Machine Learning** | Lots of Legends, International Centre for Theoretical Sciences, TIFR | [SPMML](https://www.icts.res.in/discussion-meeting/SPMML2017) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL04QVxpjcnjhtL3IIVyFRMOgdhWtPn7YJ) | 2017 | | 22. | **Lisbon Machine Learning School** | Lots of Legends, Instituto Superior Técnico, Portugal | [LxMLS-17](http://lxmls.it.pt/2017/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLToLj8M4ao-fuRfnzEJCCnvuW2_FeJ73N) | 2017 | | 23. | **Interactive Learning** | Lots of Legends, Simons Institute, Berkeley | [IL-2017](https://simons.berkeley.edu/workshops/schedule/3749) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre10T2POF-WzXh0ckdpyvANUx) | 2017 | | 24. | **Computational Challenges in Machine Learning** | Lots of Legends, Simons Institute, Berkeley | [CCML-17](https://simons.berkeley.edu/workshops/schedule/3751) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre12eXz4dnvc8oervo2_Af4iU) | 2017 | | 25. | **Foundations of Data Science** | Lots of Legends, Simons Institute | [DS-BootCamp](https://simons.berkeley.edu/workshops/abstracts/6680) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre13r1Qrnrejj3f498-NurSf3) | 2018 | | 26. | **Deep Learning and Bayesian Methods** | Lots of Legends, HSE Moscow | [DLBM-SS](http://deepbayes.ru/2018/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLe5rNUydzV9Q01vWCP9BV7NhJG3j7mz62) | 2018 | | 27. | **New Deep Learning Techniques** | Lots of Legends, IPAM UCLA | [IPAM-Workshop](https://www.ipam.ucla.edu/programs/workshops/new-deep-learning-techniques/?tab=schedule) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLHyI3Fbmv0SdM0zXj31HWjG9t9Q0v2xYN) | 2018 | | 28. | **Deep Learning and Reinforcement Learning Summer School** | Lots of Legends, University of Toronto | [DLRL-2018](https://dlrlsummerschool.ca/2018-event/) | [Lecture-videos](http://videolectures.net/DLRLsummerschool2018_toronto/) | 2018 | | 29. | **Machine Learning Summer School** | Lots of Legends, Universidad Autónoma de Madrid, Spain | [MLSS-18](http://mlss.ii.uam.es/mlss2018/index.html) | [YouTube-Lectures](https://www.youtube.com/channel/UCbPJHr__eIor_7jFH3HmVHQ/videos)
[Course-videos](http://mlss.ii.uam.es/mlss2018/speakers.html) | 2018 | | 30. | **Theoretical Basis of Machine Learning** | Lots of Legends, International Centre for Theoretical Sciences, TIFR | [TBML-18](https://www.icts.res.in/discussion-meeting/tbml2018) | [Lecture-Videos](https://www.icts.res.in/discussion-meeting/tbml2018/talks)
[YouTube-Videos](https://www.youtube.com/playlist?list=PL04QVxpjcnjj1DgnXxFBo2fkSju4r-ggr) | 2018 | | | | | | | | | 31. | **Polish View on Machine Learning** | Lots of Legends, Warsaw | [PLinML-18](https://plinml.mimuw.edu.pl/) | [YouTube-Videos](https://www.youtube.com/playlist?list=PLoaWrlj9TDhPcA6N9dZQ6GPXboYuumDRp) | 2018 | | 32. | **Big Data Analysis in Astronomy** | Lots of Legends, Tenerife | [BDAA-18](http://research.iac.es/winterschool/2018/pages/book-ws2018.php) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM4Pv4KYYzGx42W5pSp3Itetp0u-PENtI) | 2018 | | 33. | **Machine Learning Advances and Applications Seminar** | Lots of Legends, Fields Institute, University of Toronto | [MLASS](http://www.fields.utoronto.ca/activities/18-19/machine-learning) | [Video Lectures](http://www.fields.utoronto.ca/video-archive/event/2681) | 2018-2019 | | 34. | **MIFODS- ML, Stats, ToC seminar** | Lots of Legends, MIT | [MIFODS-seminar](http://mifods.mit.edu/seminar.php) | [Lecture-videos](http://mifods.mit.edu/seminar.php) | 2018-2019 | | 35. | **Learning Machines Seminar Series** | Lots of Legends, Cornell Tech | [LMSS](https://lmss.tech.cornell.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLycW2Yy79JuxbQZ9uHEu_NS3cGNomhL2A) | 2018-now | | 36. | **Machine Learning Summer School** | Lots of Legends, South Africa | [MLSS'19](https://mlssafrica.com/programme-schedule/) | [YouTube-Lectures](https://www.youtube.com/channel/UC722CmQVgcLtxt_jXr3RyWg/videos) | 2019 | | 37. | **Deep Learning Boot Camp** | Lots of Legends, Simons Institute, Berkeley | [DLBC-19](https://simons.berkeley.edu/workshops/schedule/10624) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre12c2Il9mNX0Cmp9Z4oFNrQh) | 2019 | | 38. | **Frontiers of Deep Learning** | Lots of Legends, Simons Institute, Berkeley | [FoDL-19](https://simons.berkeley.edu/workshops/schedule/10627) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre11ekU7g-Z_qsvjDD8cT-hi9) | 2019 | | 39. | **Mathematics of data: Structured representations for sensing, approximation and learning** | Lots of Legends, The Alan Turing Institute, London | [MoD-19](https://www.turing.ac.uk/sites/default/files/2019-05/agenda_9_3.pdf) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLuD_SqLtxSdX_w1Ztexpzl_EJgFQSkWez) | 2019 | | 40. | **Deep Learning and Bayesian Methods** | Lots of Legends, HSE Moscow | [DLBM-SS](http://deepbayes.ru/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLe5rNUydzV9QHe8VDStpU0o8Yp63OecdW) | 2019 | | | | | | | | | 41. | **The Mathematics of Deep Learning and Data Science** | Lots of Legends, Isaac Newton Institute, Cambridge | [MoDL-DS](https://gateway.newton.ac.uk/event/ofbw46) | [Lecture-Videos](https://gateway.newton.ac.uk/event/ofbw46/programme) | 2019 | | 42. | **Geometry of Deep Learning** | Lots of Legends, MSR Redmond | [GoDL](https://www.microsoft.com/en-us/research/event/ai-institute-2019) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLD7HFcN7LXRe30qq36It2XCljxc340O_d) | 2019 | | 43. | **Deep Learning for Science School** | Many folks, LBNL, Berkeley | [DLfSS](https://dl4sci-school.lbl.gov/agenda) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL20S5EeApOSvfvEyhCPOUzU7zkBcR5-eL) | 2019 | | 44. | **Emerging Challenges in Deep Learning** | Lots of Legends, Simons Institute, Berkeley | [ECDL](https://simons.berkeley.edu/workshops/schedule/10629) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre10BpafDrv0fg2VNUweWXWVd) | 2019 | | 45. | **Full Stack Deep Learning** | Pieter Abbeel and many others, UC Berkeley | [FSDL-M19](https://fullstackdeeplearning.com/march2019) | [YouTube-Lectures-Day-1](https://www.youtube.com/playlist?list=PL1T8fO7ArWlcf3Hc4VMEVBlH8HZm_NbeB)
[Day-2](https://www.youtube.com/playlist?list=PL1T8fO7ArWlf6TWwdstb-PcwlubnlrKrm) | 2019 | | 46. | **Algorithmic and Theoretical aspects of Machine Learning** | Lots of legends, IIIT-Bengaluru | [ACM-ML](https://india.acm.org/education/machine-learning)
[nptel](https://nptel.ac.in/courses/128/106/128106011/) | [YouTube-Lectures](https://nptel.ac.in/courses/128/106/128106011) | 2019 | | 47. | **Deep Learning and Reinforcement Learning Summer School** | Lots of Legends, AMII, Edmonton, Canada | [DLRL-2019](https://dlrlsummerschool.ca/past-years) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLKlhhkvvU8-aXmPQZNYG_e-2nTd0tJE8v) | 2019 | | 48. | **Mathematics of Machine Learning** - Summer Graduate School | Lots of Legends, University of Washington | [MoML-SGS](http://www.msri.org/summer_schools/866#schedule), [MoML-SS](http://mathofml.cs.washington.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTPQEx-31JXhguCush5J7OGnEORofoCW9) | 2019 | | 49. | **Workshop on Theory of Deep Learning: Where next?** | Lots of Legends, IAS, Princeton University | [WTDL](https://www.math.ias.edu/wtdl) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdDZb3TwJPZ5dqqg_S-rgJqSFeH4DQqFQ) | 2019 | | 50. | **Computational Vision Summer School** | Lots of Legends, Black Forest, Germany | [CVSS-2019](http://orga.cvss.cc/program-cvss-2019/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLeCNfJWZKqxsvidOlVLtWq9s7sIsX1QTC) | 2019 | | | | | | | | | 51. | **Learning under complex structure** | Lots of Legends, MIT | [LUCS](https://mifods.mit.edu/complex.php) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM4Pv4KYYzGwhIHcaY6zYR7M9hhFO4Vud) | 2020 | | 52. | **Machine Learning Summer School** | Lots of Legends, MPI-IS Tübingen (virtual) | [MLSS](http://mlss.tuebingen.mpg.de/2020/schedule.html) | [YouTube-Lectures](https://www.youtube.com/channel/UCBOgpkDhQuYeVVjuzS5Wtxw/videos) | SS2020 | | 53. | **Eastern European Machine Learning Summer School** | Lots of Legends, Kraków, Poland (virtual) | [EEML](https://www.eeml.eu/program) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLaKY4p4V3gE1j01FOY2FeglV4jRntQj84) | S2020 | | 54. | **Lisbon Machine Learning Summer School** | Lots of Legends, Lisbon, Portugal (virtual) | [LxMLS](http://lxmls.it.pt/2020/?page_id=19) | [YouTube-Lectures](https://www.youtube.com/channel/UCkVFZWgT1jR75UvSLGP9_mw) | S2020 | | 55. | **Workshop on New Directions in Optimization, Statistics and Machine Learning** | Lots of Legends, Institute of Advanced Study, Princeton | [ML-Opt new dir.](https://www.ias.edu/video/workshop/2020/0415-16) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdDZb3TwJPZ4Ri6i0MIdesIEpYK4lx17Q) | 2020 | | 56. | **Mediterranean Machine Learning School** | Lots of Legends, Italy (virtual) | [M2L-school](https://www.m2lschool.org/talks) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLF-wkqRv4u1YRbfnwN8cXXyrmXld-sked) | 2021 | | 57. | **Mathematics of Machine Learning - One World Seminar** | Lots of Legends, Virtual | [1W-ML](https://sites.google.com/view/oneworldml/past-events) | [YouTube-Lectures](https://www.youtube.com/channel/UCz7WlgXs20CzugkfxhFCNFg/videos) | 2020 - now | | 58. | **Deep Learning Theory Summer School** | Lots of Legends, Princeton University (virtual) | [DLT'21](https://deep-learning-summer-school.princeton.edu) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL2mB9GGlueJj_FNjJ8RWgz4Nc_hCSXfMU) | 2021 | | | | | | | | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ### :bird: Bird's Eye view of A(G)I :eagle: :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: | S.No | Course Name | University/Instructor(s) | Course WebPage | Lecture Videos | Year | | ---- | -------------------------------------- | -------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | --------- | | 1. | **Artificial General Intelligence** | Lots of Legends, MIT | [6.S099-AGI](https://agi.mit.edu/) | [Lecture-Videos](https://agi.mit.edu/) | 2018-2019 | | 2. | **AI Podcast** | Lots of Legends, MIT | [AI-Pod](https://lexfridman.com/ai/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4) | 2018-2019 | | 3. | **NYU - AI Seminars** | Lots of Legends, NYU | [modern-AI](https://engineering.nyu.edu/academics/departments/electrical-and-computer-engineering/ece-seminar-series/modern-artificial) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLhwo5ntex8iY9xhpSwWas451NgVuqBE7U) | 2017-now | | 4. | **Deep Learning: Alchemy or Science?** | Lots of Legends, Institute for Advanced Study, Princeton | [DLAS](https://video.ias.edu/deeplearning/2019/0222)
[Agenda](https://www.math.ias.edu/tml/dlasagenda) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdDZb3TwJPZ7aAxhIHALBoh8l6-UxmMNP) | 2019 | | | | | | | | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ### To-Do :running: :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: :white_large_square: Optimization courses which form the foundation for ML, DL, RL :white_large_square: Computer Vision courses which are DL & ML heavy :white_large_square: Speech recognition courses which are DL heavy :white_large_square: Structured Courses on Geometric, Graph Neural Networks :white_large_square: Section on Autonomous Vehicles :white_large_square: Section on Computer Graphics with ML/DL focus :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ### Around the Web :earth_asia: - [Montreal.AI](http://www.montreal.ai/ai4all.pdf) - [UPC-DLAI-2018](https://telecombcn-dl.github.io/2018-dlai/) - [UPC-DLAI-2019](https://telecombcn-dl.github.io/dlai-2019/) - [www.hashtagtechgeek.com](https://www.hashtagtechgeek.com/2019/10/250-machine-learning-deep-learning-videos-courseware.html) - [UPC-Barcelona, IDL-2020](https://telecombcn-dl.github.io/idl-2020/) - [UPC-DLAI-2020](https://telecombcn-dl.github.io/dlai-2020) :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ### Contributions :pray: If you find a course that fits in any of the above categories (i.e. DL, ML, RL, CV, NLP), **and** the course has lecture videos (with slides being optional), then please raise an issue or send a PR by updating the course according to the above format. :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ### Support :moneybag: **Optional:** If you're a kind Samaritan and want to support me, please do so if possible, for which I would eternally be thankful and, most importantly, your contribution imbues me with greater motivation to work, particularly in hard times :pray: [![](https://www.paypalobjects.com/en_US/i/btn/btn_donateCC_LG.gif)](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=NT3EATS5N35WU) Vielen lieben Dank! :blue_heart: :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ### :gift_heart: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board::mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board::mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :gift_heart: :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ================================================ FILE: markdown2html_py/README.md ================================================ # :balloon: :tada: Deep Learning Drizzle :confetti_ball: :balloon: :books: [**"Read enough so you start developing intuitions and then trust your intuitions and go for it!"** ](https://www.deeplearning.ai/hodl-geoffrey-hinton/) :books:
Prof. Geoffrey Hinton, University of Toronto :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ### Contents :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: | | | | ------------------------------------------------------------ | ------------------------------------------------------------ | | **Deep Learning (Deep Neural Networks)** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#tada-deep-learning-deep-neural-networks-confetti_ball-balloon) | **Probabilistic Graphical Models** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#loudspeaker-probabilistic-graphical-models-sparkles) | | | | | **Machine Learning Fundamentals** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#cupid-machine-learning-fundamentals-cyclone-boom) | **Natural Language Processing** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#hibiscus-natural-language-processing-cherry_blossom-sparkling_heart) | | | | | **Optimization for Machine Learning** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#cupid-optimization-for-machine-learning-cyclone-boom) | **Automatic Speech Recognition** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#speaking_head-automatic-speech-recognition-speech_balloon-thought_balloon) | | | | | **General Machine Learning** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#cupid-general-machine-learning-cyclone-boom) | **Modern Computer Vision** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#fire-modern-computer-vision-camera_flash-movie_camera) | | | | | **Reinforcement Learning** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#balloon-reinforcement-learning-hotsprings-video_game) | **Boot Camps or Summer Schools** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#star2-boot-camps-or-summer-schools-maple_leaf) | | | | | **Bayesian Deep Learning** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#game_die-bayesian-deep-learning-spades-gem) | **Medical Imaging** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#movie_camera-medical-imaging-camera-video_camera) | | | | | **Graph Neural Networks** [:arrow_heading_down: ](https://github.com/kmario23/deep-learning-drizzle#tada-graph-neural-networks-geometric-dl-confetti_ball-balloon) | **Bird's-eye view of Artificial Intelligence** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#bird-birds-eye-view-of-agi-eagle) | | | | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ## :tada: Deep Learning (Deep Neural Networks) :confetti_ball: :balloon: :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: | S.No | Course Name | University/Instructor(s) | Course WebPage | Lecture Videos | Year | | ---- | ----------------------------------------------------- | ---------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | --------------- | | 1. | **Neural Networks for Machine Learning** | Geoffrey Hinton, University of Toronto | [Lecture-Slides](http://www.cs.toronto.edu/~hinton/coursera_slides.html)
[CSC321-tijmen](https://www.cs.toronto.edu/~tijmen/csc321/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoRl3Ht4JOcdU872GhiYWf6jwrk_SNhz9)
[UofT-mirror](https://www.cs.toronto.edu/~hinton/coursera_lectures.html) | 2012
2014 | | 2. | **Neural Networks Demystified** | Stephen Welch, Welch Labs | [Suppl. Code](https://github.com/stephencwelch/Neural-Networks-Demystified) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU) | 2014 | | 3. | **Deep Learning at Oxford** | Nando de Freitas, Oxford University | [Oxford-ML](http://www.cs.ox.ac.uk/teaching/courses/2014-2015/ml/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu) | 2015 | | 4. | **Deep Learning for Perception** | Dhruv Batra, Virginia Tech | [ECE-6504](https://computing.ece.vt.edu/~f15ece6504/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL-fZD610i7yAsfH2eLBiRDa90kL2ML0f7) | 2015 | | 5. | **Deep Learning** | Ali Ghodsi, University of Waterloo | [STAT-946](https://uwaterloo.ca/data-analytics/deep-learning) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLehuLRPyt1Hyi78UOkMPWCGRxGcA9NVOE) | F2015 | | 6. | **CS231n: CNNs for Visual Recognition** | Andrej Karpathy, Stanford University | [CS231n](http://cs231n.stanford.edu/2015/) | `None` | 2015 | | 7. | **CS224d: Deep Learning for NLP** | Richard Socher, Stanford University | [CS224d](http://cs224d.stanford.edu) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLmImxx8Char8dxWB9LRqdpCTmewaml96q) | 2015 | | 8. | **Bay Area Deep Learning** | Many legends, Stanford | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLrAXtmErZgOfMuxkACrYnD2fTgbzk2THW) | 2016 | | 9. | **CS231n: CNNs for Visual Recognition** | Andrej Karpathy, Stanford University | [CS231n](http://cs231n.stanford.edu/2016/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC)
[(Academic Torrent)](https://academictorrents.com/details/46c5af9e2075d9af06f280b55b65cf9b44eb9fe7) | 2016 | | 10. | **Neural Networks** | Hugo Larochelle, Université de Sherbrooke | [Neural-Networks](http://info.usherbrooke.ca/hlarochelle/neural_networks/content.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH)
[(Academic Torrent)](https://academictorrents.com/details/e046bca3bc837053d1609ef33d623ee5c5af7300) | 2016 | | | | | | | | | 11. | **CS224d: Deep Learning for NLP** | Richard Socher, Stanford University | [CS224d](http://cs224d.stanford.edu) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLlJy-eBtNFt4CSVWYqscHDdP58M3zFHIG)
[(Academic Torrent)](https://academictorrents.com/details/dd9b74b50a1292b4b154094b7338ec1d66e8894d) | 2016 | | 12. | **CS224n: NLP with Deep Learning** | Richard Socher, Stanford University | [CS224n](http://web.stanford.edu/class/cs224n/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6) | 2017 | | 13. | **CS231n: CNNs for Visual Recognition** | Justin Johnson, Stanford University | [CS231n](http://cs231n.stanford.edu/2017/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv)
[(Academic Torrent)](https://academictorrents.com/details/ed8a16ebb346e14119a03371665306609e485f13) | 2017 | | 14. | **Topics in Deep Learning** | Ruslan Salakhutdinov, CMU | [10707](https://deeplearning-cmu-10707.github.io/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLpIxOj-HnDsOSL__Buy7_UEVQkyfhHapa) | F2017 | | 15. | **Deep Learning Crash Course** | Leo Isikdogan, UT Austin | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLWKotBjTDoLj3rXBL-nEIPRN9V3a9Cx07) | 2017 | | 16. | **Deep Learning and its Applications** | François Pitié, Trinity College Dublin | [EE4C16](https://github.com/frcs/4C16-2017) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLIo1iEzl5iB9NkulNR0X5vXN8AaEKglWT) | 2017 | | 17. | **Deep Learning** | Andrew Ng, Stanford University | [CS230](http://cs230.stanford.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rOABXSygHTsbvUz4G_YQhOb) | 2018 | | 18. | **UvA Deep Learning** | Efstratios Gavves, University of Amsterdam | [UvA-DLC](https://uvadlc.github.io/) | [Lecture-Videos](https://uvadlc.github.io/lectures-sep2018.html) | 2018 | | 19. | **Advanced Deep Learning and Reinforcement Learning** | Many legends, DeepMind | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs) | 2018 | | 20. | **Machine Learning** | Peter Bloem, Vrije Universiteit Amsterdam | [MLVU](https://mlvu.github.io/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLCof9EqayQgsORO3pFzeYZFz6cszYO0VJ) | 2018 | | | | | | | | | 21. | **Deep Learning** | Francois Fleuret, EPFL | [EE-59](https://fleuret.org/ee559-2018/dlc) | [Video-Lectures](https://fleuret.org/ee559-2018/dlc/#materials) | 2018 | | 22. | **Introduction to Deep Learning** | Alexander Amini, Harini Suresh and others, MIT | [6.S191](http://introtodeeplearning.com/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI)
[2017-version](https://www.youtube.com/playlist?list=PLkkuNyzb8LmxFutYuPA7B4oiMn6cjD6Rs) | 2017- 2021 | | 23. | **Deep Learning for Self-Driving Cars** | Lex Fridman, MIT | [6.S094](https://selfdrivingcars.mit.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf) | 2017-2018 | | 24. | **Introduction to Deep Learning** | Bhiksha Raj and many others, CMU | [11-485/785](http://deeplearning.cs.cmu.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLp-0K3kfddPwJBJ4Q8We-0yNQEG0fZrSa) | S2018 | | 25. | **Introduction to Deep Learning** | Bhiksha Raj and many others, CMU | [11-485/785](http://deeplearning.cs.cmu.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLp-0K3kfddPyH44FP0dl0CbYprvTcfgOI) [Recitation-Inclusive](https://www.youtube.com/playlist?list=PLLR0_ZOlbfD6KDBq93G8-guHI-J1ICeFm) | F2018 | | 26. | **Deep Learning Specialization** | Andrew Ng, Stanford | [DL.AI](https://www.deeplearning.ai/deep-learning-specialization/) | [YouTube-Lectures](https://www.youtube.com/channel/UCcIXc5mJsHVYTZR1maL5l9w/playlists) | 2017-2018 | | 27. | **Deep Learning** | Ali Ghodsi, University of Waterloo | [STAT-946](https://uwaterloo.ca/data-analytics/teaching/deep-learning-2017) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLehuLRPyt1HxTolYUWeyyIoxDabDmaOSB) | F2017 | | 28. | **Deep Learning** | Mitesh Khapra, IIT-Madras | [CS7015](https://www.cse.iitm.ac.in/~miteshk/CS7015.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLyqSpQzTE6M9gCgajvQbc68Hk_JKGBAYT) | 2018 | | 29. | **Deep Learning for AI** | UPC Barcelona | [DLAI-2017](https://telecombcn-dl.github.io/2017-dlai/)
[DLAI-2018](https://telecombcn-dl.github.io/2018-dlai/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL-5eMc3HQTBagIUjKefjcTbnXC0wXC_vd) | 2017-2018 | | 30. | **Deep Learning** | Alex Bronstein and Avi Mendelson, Technion | [CS236605](https://vistalab-technion.github.io/cs236605/info/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM0a6Z788YAZuqg2Ip-_dPLzEd33lZvP2) | 2018 | | | | | | | | | 31. | **MIT Deep Learning** | Many Researchers, Lex Fridman, MIT | [6.S094, 6.S091, 6.S093](https://deeplearning.mit.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf) | 2019 | | 32. | **Deep Learning Book** companion videos | Ian Goodfellow and others | [DL-book slides](https://www.deeplearningbook.org/lecture_slides.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLsXu9MHQGs8df5A4PzQGw-kfviylC-R9b) | 2017 | | 33. | **Theories of Deep Learning** | Many Legends, Stanford | [Stats-385](https://stats385.github.io/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLwUqqMt5en7fFLwSDa9V3JIkDam-WWgqy)
(first 10 lectures) | F2017 | | 34. | **Neural Networks** | Grant Sanderson | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi) | 2017-2018 | | 35. | **CS230: Deep Learning** | Andrew Ng, Kian Katanforoosh, Stanford | [CS230](http://cs230.stanford.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rOABXSygHTsbvUz4G_YQhOb) | A2018 | | 36. | **Theory of Deep Learning** | Lots of Legends, Canary Islands | [DALI'18](http://dalimeeting.org/dali2018/workshopTheoryDL.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLeCNfJWZKqxtWBnV8gefGqmmPgz9YF4LR) | 2018 | | 37. | **Introduction to Deep Learning** | Alex Smola, UC Berkeley | [Stat-157](http://courses.d2l.ai/berkeley-stat-157/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZSO_6-bSqHQHBCoGaObUljoXAyyqhpFW) | S2019 | | 38. | **Deep Unsupervised Learning** | Pieter Abbeel, UC Berkeley | [CS294-158](https://sites.google.com/view/berkeley-cs294-158-sp19/home) | [YouTube-Lectures](https://www.youtube.com/channel/UCf4SX8kAZM_oGcZjMREsU9w/videos) | S2019 | | 39. | **Machine Learning** | Peter Bloem, Vrije Universiteit Amsterdam | [MLVU](https://mlvu.github.io/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLCof9EqayQgupldnTvqNy_BThTcME5r93) | 2019 | | 40. | **Deep Learning on Computational Accelerators** | Alex Bronstein and Avi Mendelson, Technion | [CS236605](https://vistalab-technion.github.io/cs236605/lectures/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM0a6Z788YAa_WCy_V-q9NrGm5qQegZR5) | S2019 | | | | | | | | | 41. | **Introduction to Deep Learning** | Bhiksha Raj and many others, CMU | [11-785](http://www.cs.cmu.edu/~bhiksha/courses/deeplearning/Spring.2019/www) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLp-0K3kfddPzNdZPX4p0lVi6AcDXBofuf) | S2019 | | 42. | **Introduction to Deep Learning** | Bhiksha Raj and many others, CMU | [11-785](https://www.cs.cmu.edu/~bhiksha/courses/deeplearning/Fall.2019/www) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLp-0K3kfddPwz13VqV1PaMXF6V6dYdEsj)
[Recitations](https://www.youtube.com/playlist?list=PLp-0K3kfddPxf4T59JEQKv5UanLPVsxzz) | F2019 | | 43. | **UvA Deep Learning** | Efstratios Gavves, University of Amsterdam | [UvA-DLC](https://uvadlc.github.io/) | [Lecture-Videos](https://uvadlc.github.io/lectures-apr2019.html) | S2019 | | 44. | **Deep Learning** | Prabir Kumar Biswas, IIT Kgp | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLbRMhDVUMngc7NM-gDwcBzIYZNFSK2N1a) | 2019 | | 45. | **Deep Learning and its Applications** | Aditya Nigam, IIT Mandi | [CS-671](http://faculty.iitmandi.ac.in/~aditya/cs671/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLKvX2d3IUq586Ic9gIhZj6ubpWV-OJfl4) | 2019 | | 46. | **Neural Networks** | Neil Rhodes, Harvey Mudd College | [CS-152](https://www.cs.hmc.edu/~rhodes/cs152/schedule.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgEuVSRbAI9UIQSHGy4l01laA_12YOqEj) | F2019 | | 47. | **Deep Learning** | Thomas Hofmann, ETH Zürich | [DAL-DL](http://www.da.inf.ethz.ch/teaching/2019/DeepLearning) | [Lecture-Videos](https://video.ethz.ch/lectures/d-infk/2019/autumn/263-3210-00L.html) | F2019 | | 48. | **Deep Learning** | Milan Straka, Charles University | [NPFL114](https://ufal.mff.cuni.cz/courses/npfl114) | [Lecture-Videos](https://ufal.mff.cuni.cz/courses/npfl114/1718-summer) | S2019 | | 49. | **UvA Deep Learning** | Efstratios Gavves, University of Amsterdam | [UvA-DLC-19](https://uvadlc.github.io/#lectures) | [Lecture-Videos](https://uvadlc.github.io/#lectures) | F2019 | | 50. | **Artificial Intelligence: Principles and Techniques** | Percy Liang and Dorsa Sadigh, Stanford University | [CS221](https://stanford-cs221.github.io/autumn2019/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rO1NB9TD4iUZ3qghGEGtqNX) | F2019 | | | | | | | | | 51. | **Analyses of Deep Learning** | Lots of Legends, Stanford University | [STATS-385](https://stats385.github.io/) | [YouTube-Lectures](https://stats385.github.io/lecture_videos) | 2017-2019 | | 52. | **Deep Learning Foundations and Applications** | Debdoot Sheet and Sudeshna Sarkar, IIT-Kgp | [AI61002](http://www.facweb.iitkgp.ac.in/~debdoot/courses/AI61002/Spr2020) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_AdDfjIMo6pZfwjZ0rJlkE_MIsmRW7Mh) | S2020 | | 53. | **Designing, Visualizing, and Understanding Deep Neural Networks** | John Canny, UC Berkeley | [CS 182/282A](https://bcourses.berkeley.edu/courses/1487769/pages/cs-l-w-182-slash-282a-designing-visualizing-and-understanding-deep-neural-networks-spring-2020) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLkFD6_40KJIwaO6Eca8kzsEFBob0nFvwm) | S2020 | | 54. | **Deep Learning** | Yann LeCun and Alfredo Canziani, NYU | [DS-GA 1008](https://atcold.github.io/pytorch-Deep-Learning/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq) | S2020 | | 55. | **Introduction to Deep Learning** | Bhiksha Raj, CMU | [11-785](https://deeplearning.cs.cmu.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLp-0K3kfddPzCnS4CqKphh-zT3aDwybDe) | S2020 | | 56. | **Deep Unsupervised Learning** | Pieter Abbeel, UC Berkeley | [CS294-158](https://sites.google.com/view/berkeley-cs294-158-sp20) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLwRJQ4m4UJjPiJP3691u-qWwPGVKzSlNP) | S2020 | | 57. | **Machine Learning** | Peter Bloem, Vrije Universiteit Amsterdam | [VUML](https://mlvu.github.io/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLCof9EqayQgthR7IViXkAkUwel_rhxGYM) | S2020 | | 58. | **Deep Learning (with PyTorch)** | Alfredo Canziani and Yann LeCun, NYU | [DS-GA 1008](https://atcold.github.io/pytorch-Deep-Learning/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq) | S2020 | | 59. | **Introduction to Deep Learning and Generative Models** | Sebastian Raschka, UW-Madison | [Stat453](http://pages.stat.wisc.edu/~sraschka/teaching/stat453-ss2020/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTKMiZHVd_2JkR6QtQEnml7swCnFBtq4P) | S2020 | | 60. | **Deep Learning** | Andreas Maier, FAU Erlangen-Nürnberg | [DL-2020](https://www.video.uni-erlangen.de/course/id/925) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLpOGQvPCDQzvgpD3S0vTy7bJe2pf_yJFj)
[Lecture-Videos](https://www.video.uni-erlangen.de/course/id/925) | SS2020 | | | | | | | | | 61. | **Introduction to Deep Learning** | Laura Leal-Taixé and Matthias Niessner, TU-München | [I2DL-IN2346](https://dvl.in.tum.de/teaching/i2dl-ss20/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLQ8Y4kIIbzy_OaXv86lfbQwPHSomk2o2e) | SS2020 | | 62. | **Deep Learning** | Sargur Srihari, SUNY-Buffalo | [CSE676](https://cedar.buffalo.edu/~srihari/CSE676/) | [YouTube-Lectures-P1](https://www.youtube.com/playlist?list=PLmx4utxjUQD70k_NzeiSIXf30m54T_e1h)
[YouTube-Lectures-P2](https://www.youtube.com/channel/UCUm7yUmVJyAbYh_0ppJ4H-g/videos) | 2020 | | 63. | **Deep Learning Lecture Series** | Lots of Legends, DeepMind x UCL, London | [DLLS-20](https://deepmind.com/learning-resources/deep-learning-lecture-series-2020) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLqYmG7hTraZCDxZ44o4p3N5Anz3lLRVZF) | 2020 | | 64. | **MultiModal Machine Learning** | Louis-Philippe Morency & others, Carnegie Mellon University | [11-777 MMML-20](https://cmu-multicomp-lab.github.io/mmml-course/fall2020) | [YouTube-Lectures](https://www.youtube.com/channel/UCqlHIJTGYhiwQpNuPU5e2gg/videos) | F2020 | | 65. | **Reliable and Interpretable Artificial Intelligence** | Martin Vechev, ETH Zürich | [RIAI-20](https://www.sri.inf.ethz.ch/teaching/riai2020) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLWjm4hHpaNg6c-W7JjNYDEC_kJK9oSp0Y) | F2020 | | 66. | **Fundamentals of Deep Learning** | David McAllester, Toyota Technological Institute, Chicago | [TTIC-31230](https://mcallester.github.io/ttic-31230/Fall2020) | [YouTube-Lectures](https://www.youtube.com/channel/UCciVrtrRR3bQdaGbti9-hVQ/videos) | F2020 | | 67. | **Foundations of Deep Learning** | Soheil Feize, University of Maryland, College Park | [CMSC 828W](http://www.cs.umd.edu/class/fall2020/cmsc828W) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLHgjs9ncvHi80UCSlSvQe-TK_uOyDv_Jf) | F2020 | | 68. | **Deep Learning** | Andreas Geiger, Universität Tübingen | [DL-UT](https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/autonomous-vision/teaching/lecture-deep-learning/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij3NTWIdtMbfvX7Z-4WEXRqD) | W20/21 | | 69. | **Deep Learning** | Andreas Maier, FAU Erlangen-Nürnberg | [DL-FAU](https://www.fau.tv/course/id/1599) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLpOGQvPCDQzvJEPFUQ3mJz72GJ95jyZTh) | W20/21 | | 70. | **Fundamentals of Deep Learning** | Terence Parr and Yannet Interian, University of San Francisco | [DL-Fundamentals](https://github.com/parrt/fundamentals-of-deep-learning) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLFCc_Fc116ikeol9CZcWWKqmrJljxhE4N) | S2021 | | | | | | | | | 71. | **Full Stack Deep Learning** | Pieter Abbeel, Sergey Karayev, UC Berkeley | [FS-DL](https://fullstackdeeplearning.com/spring2021) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL1T8fO7ArWlcWg04OgNiJy91PywMKT2lv) | S2021 | | 72. | **Deep Learning: Designing, Visualizing, and Understanding DNNs** | Sergey Levine, UC Berkeley | [CS 182](https://cs182sp21.github.io) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_iWQOsE6TfVmKkQHucjPAoRtIJYt8a5A) | S2021 | | 73. | **Deep Learning in the Life Sciences** | Manolis Kellis, MIT | [6.874](https://mit6874.github.io) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLypiXJdtIca5sxV7aE3-PS9fYX3vUdIOX) | S2021 | | 74. | **Introduction to Deep Learning and Generative Models** | Sebastian Raschka, University of Wisconsin-Madison | [Stat 453](http://pages.stat.wisc.edu/~sraschka/teaching/stat453-ss2021) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTKMiZHVd_2KJtIXOW0zFhFfBaJJilH51) | S2021 | | 75. | **Deep Learning** | Alfredo Canziani and Yann LeCun, NYU | [NYU-DLSP21](https://atcold.github.io/NYU-DLSP21) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLLHTzKZzVU9e6xUfG10TkTWApKSZCzuBI) | S2021 | | 76. | **Applied Deep Learning** | Alexander Pacha, TU Wien | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLNsFwZQ_pkE8xNYTEyorbaWPN7nvbWyk1) | 2020-2021 | | 77. | **Machine Learning** | Hung-yi Lee, National Taiwan University | [ML'21](https://speech.ee.ntu.edu.tw/~hylee/ml/2021-spring.php) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLJV_el3uVTsNxV_IGauQZBHjBKZ26JHjd) | S2021 | | 78. | **Mathematics of Deep Learning** | Lots of legends, FAU | [MoDL](https://www.fau.tv/course/id/878) | [Lecture-Videos](https://www.fau.tv/course/id/878) | 2019-21 | | 79. | **Deep Learning** | Peter Bloem, Michael Cochez, and Jakub Tomczak, VU-Amsterdam | [DL](https://dlvu.github.io/) | [YouTube-Lectures](https://www.youtube.com/channel/UCYh1zKnwzrSjrO2Ae-akfTg/playlists) | 2020-21 | | 80. | **Applied Deep Learning** | Maziar Raissi, UC Boulder | [ADL'21](https://github.com/maziarraissi/Applied-Deep-Learning) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoEMreTa9CNmuxQeIKWaz7AVFd_ZeAcy4) | 2021 | | | | | | | | | 81. | **An Introduction to Group Equivariant Deep Learning** | Erik J. Bekkers, Universiteit van Amsterdam | [UvAGEDL](https://uvagedl.github.io) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL8FnQMH2k7jzPrxqdYufoiYVHim8PyZWd) | 2022 | | | | | | | | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ### :cupid: Machine Learning Fundamentals :cyclone: :boom: :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: | S.No | Course Name | University/Instructor(s) | Course Webpage | Video Lectures | Year | | ---- | ------------------------------------------------------------ | ------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ---------- | | 1. | **Linear Algebra** | Gilbert Strang, MIT | [18.06 SC](http://ocw.mit.edu/18-06SCF11) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL221E2BBF13BECF6C) | 2011 | | 2. | **Probability Primer** | Jeffrey Miller, Brown University | `mathematical monk` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL17567A1A3F5DB5E4) | 2011 | | 3. | **Information Theory, Pattern Recognition, and Neural Networks** | David Mackay, University of Cambridge | [ITPRNN](http://www.inference.org.uk/mackay/itprnn) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLruBu5BI5n4aFpG32iMbdWoRVAA-Vcso6) | 2012 | | 4. | **Linear Algebra Review** | Zico Kolter, CMU | [LinAlg](http://www.cs.cmu.edu/~zkolter/course/linalg/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM4Pv4KYYzGzL5ay6dmpyzRnbzQ__8v_t) | 2013 | | 5. | **Probability and Statistics** | Michel van Biezen | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLX2gX-ftPVXUWwTzAkOhBdhplvz0fByqV) | 2015 | | 6. | **Linear Algebra: An in-depth Introduction** | Pavel Grinfeld | `None` | [Part-1](https://www.youtube.com/playlist?list=PLlXfTHzgMRUKXD88IdzS14F4NxAZudSmv)
[Part-2](https://www.youtube.com/playlist?list=PLlXfTHzgMRULWJYthculb2QWEiZOkwTSU)
[Part-3](https://www.youtube.com/playlist?list=PLlXfTHzgMRUIqYrutsFXCOmiqKUgOgGJ5)
[Part-4](https://www.youtube.com/playlist?list=PLlXfTHzgMRULZfrNCrrJ7xDcTjGr633mm) | 2015- 2017 | | 7. | **Multivariable Calculus** | Grant Sanderson, Khan Academy | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLSQl0a2vh4HC5feHa6Rc5c0wbRTx56nF7) | 2016 | | 8. | **Essence of Linear Algebra** | Grant Sanderson | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) | 2016 | | 9. | **Essence of Calculus** | Grant Sanderson | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr) | 2017-2018 | | 10. | **Math Background for Machine Learning** | Geoff Gordon, CMU | [10-606](https://canvas.cmu.edu/courses/603/assignments/syllabus), [10-607](https://piazza.com/cmu/fall2017/1060610607/home) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL7y-1rk2cCsAqRtWoZ95z-GMcecVG5mzA) | F2017 | | | | | | | | | 11. | **Mathematics for Machine Learning** (Linear Algebra, Calculus) | David Dye, Samuel Cooper, and Freddie Page, IC-London | [MML](https://www.coursera.org/learn/linear-algebra-machine-learning) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLmAuaUS7wSOP-iTNDivR0ANKuTUhEzMe4) | 2018 | | 12. | **Multivariable Calculus** | S.K. Gupta and Sanjeev Kumar, IIT-Roorkee | [MVC](https://nptel.ac.in/syllabus/111107108/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLq-Gm0yRYwTiQtK374NzhFOcQkWmJ71vx) | 2018 | | 13. | **Engineering Probability** | Rich Radke, Rensselaer Polytechnic Institute | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLuh62Q4Sv7BU1dN2G6ncyiMbML7OXh_Jx) | 2018 | | 14. | **Matrix Methods in Data Analysis, Signal Processing, and Machine Learning** | Gilbert Strang, MIT | [18.065](https://ocw.mit.edu/courses/mathematics/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k) | S2018 | | 15. | **Information Theory** | Himanshu Tyagi, IISC, Bengaluru | [E2 201](https://ece.iisc.ac.in/~htyagi/course-E2201-2020.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgMDNELGJ1CYS-8dlMGPIaowVfeda4nUj) | 2018-20 | | 16. | **Math Camp** | Mark Walker, University of Arizona | [UAMathCamp / Econ-519](http://www.u.arizona.edu/~mwalker/MathCamp2019.htm) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLcjqUUQt__ZGLhwUacPm7_RKs2eJNFwco) | 2019 | | 17. | **A 2020 Vision of Linear Algebra** | Gilbert Strang, MIT | [VoLA](https://ocw.mit.edu/resources/res-18-010-a-2020-vision-of-linear-algebra-spring-2020/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLUl4u3cNGP61iQEFiWLE21EJCxwmWvvek) | S2020 | | 18. | **Mathematics for Numerical Computing and Machine Learning** | Szymon Rusinkiewicz, Princeton University | [COS-302](https://www.cs.princeton.edu/courses/archive/fall20/cos302/outline.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL88aSuXxl_dSjC5pIG8bGkC5wsUPyW_Hh) | F2020 | | 19. | **Essential Statistics for Neuroscientists** | Philipp Berens, Universität Klinikum Tübingen | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij0Gw5SLIrOA1dMYScCx4oXT) | 2020 | | 20. | **Mathematics for Machine Learning** | Ulrike von Luxburg, Eberhard Karls Universität Tübingen | [Math4ML](https://www.tml.cs.uni-tuebingen.de/teaching/2020_maths_for_ml) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij1a6KdEy8PVE9zoCv6SlHRS) | W2020 | | 21. | **Introduction to Causal Inference** | Brady Neal, Mila, Montréal | [CausalInf](https://www.bradyneal.com/causal-inference-course) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0) | F2020 | | 22. | **Applied Linear Algebra** | Andrew Thangaraj, IIT Madras | [EE5120](http://www.ee.iitm.ac.in/~andrew/EE5120) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLyqSpQzTE6M-CHZU5RGfamcXOnuFyTOpm) | 2021 | | 23. | **Mathematical Tools for Data Science** | Carlos Fernandez-Granda, New York University | [DS-GA 1013/Math-GA 2824](https://cds.nyu.edu/math-tools) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLBEf5mJtE6KtU6YlXFZD6lyYcHhW5pIlc) | 2021 | | 24. | **Mathematics for Numerical Computing and Machine Learning** | Ryan Adams, Princeton University | [COS 302 / SML 305](https://www.cs.princeton.edu/courses/archive/spring21/cos302) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLCO4cUaBLHFEHo42HVIVWaSOvbAiH30uc) | 2021 | | | | | | | | | | | | | | | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ### :cupid: Optimization for Machine Learning :cyclone: :boom: :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: | S.No | Course Name | University/Instructor(s) | Course Webpage | Video Lectures | Year | | ---- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ---------- | | 1. | **Convex Optimization** | Stephen Boyd, Stanford University | [ee364a](http://web.stanford.edu/class/ee364a/lectures.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL3940DD956CDF0622) | 2008 | | 2. | **Introduction to Optimization** | Michael Zibulevsky, Technion | [CS-236330](https://sites.google.com/site/michaelzibulevsky/optimization-course) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLDFB2EEF4DDAFE30B) | 2009 | | 3. | **Optimization for Machine Learning** | S V N Vishwanathan, Purdue University | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL09B0E8AFC69BE108) | 2011 | | 4. | **Optimization** | Geoff Gordon & Ryan Tibshirani, CMU | [10-725](https://www.cs.cmu.edu/~ggordon/10725-F12/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL7y-1rk2cCsDOv91McLOnV4kExFfTB7dU) | 2012 | | 5. | **Convex Optimization** | Joydeep Dutta, IIT-Kanpur | [cvx-nptel](https://nptel.ac.in/courses/111/104/111104068) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLbMVogVj5nJQHFqfiSdgaLCCWvDcm1W4l) | 2013 | | 6. | **Foundations of Optimization** | Joydeep Dutta, IIT-Kanpur | [fop-nptel](https://nptel.ac.in/courses/111/104/111104071) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLbMVogVj5nJRRbofh3Qm3P6_NVyevDGD_) | 2014 | | 7. | **Algorithmic Aspects of Machine Learning** | Ankur Moitra, MIT | [18.409-AAML](http://people.csail.mit.edu/moitra/409.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLB3sDpSRdrOvI1hYXNsa6Lety7K8FhPpx) | S2015 | | 8. | **Numerical Optimization** | Shirish K. Shevade, IISC | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL6EA0722B99332589) | 2015 | | 9. | **Convex Optimization** | Ryan Tibshirani, CMU | [10-725](https://www.stat.cmu.edu/~ryantibs/convexopt-S15/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLjbUi5mgii6BZBhJ9nW7eydgycyCOYeZ6) | S2015 | | 10. | **Convex Optimization** | Ryan Tibshirani, CMU | [10-725](http://stat.cmu.edu/~ryantibs/convexopt-F15/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLjbUi5mgii6AGJW3La3BpEXe27n8v3biT) | F2015 | | 11. | **Advanced Algorithms** | Ankur Moitra, MIT | [6.854-AA](http://people.csail.mit.edu/moitra/854.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL6ogFv-ieghdoGKGg2Bik3Gl1glBTEu8c) | S2016 | | 12. | **Introduction to Optimization** | Michael Zibulevsky, Technion | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLBD31626529B0AC2A) | 2016 | | 13. | **Convex Optimization** | Javier Peña & Ryan Tibshirani | [10-725/36-725](https://www.stat.cmu.edu/~ryantibs/convexopt-F16) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLjbUi5mgii6AVdvImLB9-Hako68p9MpIC) | F2016 | | 14. | **Convex Optimization** | Ryan Tibshirani, CMU | [10-725](https://www.stat.cmu.edu/~ryantibs/convexopt-F18/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLpIxOj-HnDsMM7BCNGC3hPFU3DfCWfVIw)
[Lecture-Videos](https://www.stat.cmu.edu/~ryantibs/convexopt-F18/) | F2018 | | 15. | **Modern Algorithmic Optimization** | Yurii Nesterov, UCLouvain | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLEqoHzpnmTfAoUDqnmMly-KgyJ6ZM_axf) | 2018 | | 16. | **Optimization, Foundations of Optimization** | Mark Walker, University of Arizona | [MathCamp-20](http://www.u.arizona.edu/~mwalker/MathCamp2020/MathCamp2020LectureNotes.htm) | [YouTube-Lectures-Found.](https://www.youtube.com/playlist?list=PLcjqUUQt__ZE6wp_c4-FcRdmzBvx8VN7O)
[YouTube-Lectures-Opt](https://www.youtube.com/playlist?list=PLcjqUUQt__ZE0ZSTNRyBIgLJ5obPHdmxC) | 2019 - now | | 17. | **Optimization: Principles and Algorithms** | Michel Bierlaire, École polytechnique fédérale de Lausanne (EPFL) | [opt-algo](https://transp-or.epfl.ch/books/optimization/html/about_book.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM4Pv4KYYzGzOpWwsaV6GgllT6njsi1G-) | 2019 | | 18. | **Optimization and Simulation** | Michel Bierlaire, École polytechnique fédérale de Lausanne (EPFL) | [opt-sim](https://transp-or.epfl.ch/courses/OptSim2019/slides.php) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL10NOnsbP5Q5NlJ-Y6Eiup6RTSfkuj1TR) | S2019 | | 19. | **Brazilian Workshop on Continuous Optimization** | Lots of Legends, Instituto Nacional de Matemática Pura e Aplicada, Rio de Janeiro | [cont. opt.](https://impa.br/eventos-do-impa/eventos-2019/xiii-brazilian-workshop-on-continuous-optimization) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLo4jXE-LdDTQVZhnLPq2W31vJ1fq1VSp6) | 2019 | | 20. | **One World Optimization Seminar** | Lots of Legends, Universität Wien | [1W-OPT](https://owos.univie.ac.at) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLBQo-yZOMzLWEcAptzTYOnwXo9hhXrAa2) | 2020- | | | | | | | | | 21. | **Convex Optimization II** | Constantine Caramanis, UT Austin | [CVX-Optim-II](http://users.ece.utexas.edu/~cmcaram/constantine_caramanis/Announcements.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLXsmhnDvpjORzPelSDs0LSDrfJcqyLlZc) | S2020 | | 22. | **Combinatorial Optimization** | Constantine Caramanis, UT Austin | [comb-op](https://caramanis.github.io/teaching/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLXsmhnDvpjORcTRFMVF3aUgyYlHsxfhNL) | F2020 | | 23. | **Optimization Methods for Machine Learning and Engineering** | Julius Pfrommer, Jürgen Beyerer, Karlsruher Institut für Technologie (KIT) | [Optim-MLE](https://ies.anthropomatik.kit.edu/lehre_1487.php), [slides](https://drive.google.com/drive/folders/1WWVWV4vDBIOkjZc6uFY3nfXvpaOUHcfb) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdkTDauaUnQpzuOCZyUUZc0lxf4-PXNR5) | W2020-21 | | | | | | | | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ### :cupid: General Machine Learning :cyclone: :boom: :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: | S.No | Course Name | University/Instructor(s) | Course Webpage | Video Lectures | Year | | ---- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | --------- | | 1. | **CS229: Machine Learning** | Andrew Ng, Stanford University | [CS229-old](https://see.stanford.edu/Course/CS229/)
[CS229-new](http://cs229.stanford.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLA89DCFA6ADACE599) | 2007 | | 2. | **Machine Learning** | Jeffrey Miller, Brown University | `mathematical monk` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLD0F06AA0D2E8FFBA) | 2011 | | 3. | **Machine Learning** | Tom Mitchell, CMU | [10-701](http://www.cs.cmu.edu/~tom/10701_sp11/) | [Lecture-Videos](http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml) | 2011 | | 4. | **Machine Learning and Data Mining** | Nando de Freitas, University of British Columbia | [CPSC-340](https://www.cs.ubc.ca/~nando/340-2012/index.php) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLE6Wd9FR--Ecf_5nCbnSQMHqORpiChfJf) | 2012 | | 5. | **Learning from Data** | Yaser Abu-Mostafa, CalTech | [CS156](http://work.caltech.edu/telecourse.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLD63A284B7615313A) | 2012 | | 6. | **Machine Learning** | Rudolph Triebel, Technische Universität München | [Machine Learning](https://vision.in.tum.de/teaching/ws2013/ml_ws13) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTBdjV_4f-EIiongKlS9OKrBEp8QR47Wl) | 2013 | | 7. | **Introduction to Machine Learning** | Alex Smola, CMU | [10-701](http://alex.smola.org/teaching/cmu2013-10-701/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZSO_6-bSqHQmMKwWVvYwKreGu4b4kMU9) | 2013 | | 8. | **Introduction to Machine Learning** | Alex Smola and Geoffrey Gordon, CMU | [10-701x](http://alex.smola.org/teaching/cmu2013-10-701x/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZSO_6-bSqHR7NPk4k0zqdm2dPdraQZ_B) | 2013 | | 9. | **Pattern Recognition** | Sukhendu Das, IIT-M and C.A. Murthy, ISI-Calcutta | [PR-NPTEL](https://nptel.ac.in/syllabus/106106046/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLbMVogVj5nJQJMLb2CYw9rry0d5s0TQRp) | 2014 | | 10. | **An Introduction to Statistical Learning with Applications in R** | Trevor Hastie and Robert Tibshirani, Stanford | [stat-learn](https://lagunita.stanford.edu/courses/HumanitiesandScience/StatLearning/Winter2015/about)
[R-bloggers](https://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLOg0ngHtcqbPTlZzRHA2ocQZqB1D_qZ5V) | 2014 | | | | | | | | | 11. | **Introduction to Machine Learning** | Katie Malone, Sebastian Thrun, Udacity | [ML-Udacity](https://www.udacity.com/course/ud120) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLAwxTw4SYaPkQXg8TkVdIvYv4HfLG7SiH) | 2015 | | 12. | **Introduction to Machine Learning** | Dhruv Batra, Virginia Tech | [ECE-5984](https://filebox.ece.vt.edu/~s15ece5984/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL-fZD610i7yDUiNTFy-tEOxkTwg4mHZHu) | 2015 | | 13. | **Statistical Learning - Classification** | Ali Ghodsi, University of Waterloo | [STAT-441](https://uwaterloo.ca/data-analytics/statistical-learning-classification) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLehuLRPyt1Hy-4ObWBK4Ab0xk97s6imfC) | 2015 | | 14. | **Machine Learning Theory** | Shai Ben-David, University of Waterloo | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLPW2keNyw-usgvmR7FTQ3ZRjfLs5jT4BO) | 2015 | | 15. | **Introduction to Machine Learning** | Alex Smola, CMU | [10-701](http://alex.smola.org/teaching/10-701-15/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZSO_6-bSqHTTV7w9u7grTXBHMH-mw3qn) | S2015 | | 16. | **Statistical Machine Learning** | Larry Wasserman, CMU | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLjbUi5mgii6BWEUZf7He6nowWvGne_Y8r) | S2015 | | 17. | **ML: Supervised Learning** | Michael Littman, Charles Isbell, Pushkar Kolhe, GaTech | [ML-Udacity](https://eu.udacity.com/course/machine-learning--ud262) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLAwxTw4SYaPl0N6-e1GvyLp5-MUMUjOKo) | 2015 | | 18. | **ML: Unsupervised Learning** | Michael Littman, Charles Isbell, Pushkar Kolhe, GaTech | [ML-Udacity](https://eu.udacity.com/course/machine-learning-unsupervised-learning--ud741) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLAwxTw4SYaPmaHhu-Lz3mhLSj-YH-JnG7) | 2015 | | 19. | **Advanced Introduction to Machine Learning** | Barnabas Poczos and Alex Smola | [10-715](https://www.cs.cmu.edu/~bapoczos/Classes/ML10715_2015Fall/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL4YhK0pT0ZhWBzSBkMGzpnPw6sf6Ma0IX) | F2015 | | 20. | **Machine Learning** | Pedro Domingos, UWashington | [CSEP-546](https://courses.cs.washington.edu/courses/csep546/16sp/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTPQEx-31JXgtDaC6-3HxWcp7fq4N8YGr) | S2016 | | | | | | | | | 21. | **Statistical Machine Learning** | Larry Wasserman, CMU | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTB9VQq8WiaCBK2XrtYn5t9uuPdsNm7YE) | S2016 | | 22. | **Machine Learning with Large Datasets** | William Cohen, CMU | [10-605](http://curtis.ml.cmu.edu/w/courses/index.php/Machine_Learning_with_Large_Datasets_10-605_in_Fall_2016) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLnfBqXRW5MRhPtfkadfwQ0VcuSi2IwEcW) | F2016 | | 23. | **Math Background for Machine Learning** | Geoffrey Gordon, CMU | `10-600` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL7y-1rk2cCsA339crwXMWUaBRuLBvPBCg) | F2016 | | 24. | **Statistical Learning - Classification** | Ali Ghodsi, University of Waterloo | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG) | 2017 | | 25. | **Machine Learning** | Andrew Ng, Stanford University | [Coursera-ML](https://www.coursera.org/learn/machine-learning) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN) | 2017 | | 26. | **Machine Learning** | Roni Rosenfield, CMU | [10-601](http://www.cs.cmu.edu/~roni/10601-f17/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL7k0r4t5c10-g7CWCnHfZOAxLaiNinChk) | 2017 | | 27. | **Statistical Machine Learning** | Ryan Tibshirani, Larry Wasserman, CMU | [10-702](http://www.stat.cmu.edu/~ryantibs/statml/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLjbUi5mgii6B7A0nM74zHTOVQtTC9DaCv) | S2017 | | 28. | **Machine Learning for Computer Vision** | Fred Hamprecht, Heidelberg University | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLuRaSnb3n4kSQFyt8VBldsQ9pO9Xtu8rY) | F2017 | | 29. | **Math Background for Machine Learning** | Geoffrey Gordon, CMU | [10-606 / 10-607](https://canvas.cmu.edu/courses/603/assignments/syllabus) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL7y-1rk2cCsAqRtWoZ95z-GMcecVG5mzA) | F2017 | | 30. | **Data Visualization** | Ali Ghodsi, University of Waterloo | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLehuLRPyt1HzQoXEhtNuYTmd0aNQvtyAK) | 2017 | | | | | | | | | 31. | **Machine Learning for Physicists** | Florian Marquardt, Uni Erlangen-Nürnberg | [ML4Phy-17](http://www.thp2.nat.uni-erlangen.de/index.php/2017_Machine_Learning_for_Physicists,_by_Florian_Marquardt) | [Lecture-Videos](https://www.video.uni-erlangen.de/course/id/574) | 2017 | | 32. | **Machine Learning for Intelligent Systems** | Kilian Weinberger, Cornell University | [CS4780](http://www.cs.cornell.edu/courses/cs4780/2018fa/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLl8OlHZGYOQ7bkVbuRthEsaLr7bONzbXS) | F2018 | | 33. | **Statistical Learning Theory and Applications** | Tomaso Poggio, Lorenzo Rosasco, Sasha Rakhlin | [9.520/6.860](https://cbmm.mit.edu/lh-9-520) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLyGKBDfnk-iAtLO6oLW4swMiQGz4f2OPY) | F2018 | | 34. | **Machine Learning and Data Mining** | Mike Gelbart, University of British Columbia | [CPSC-340](https://ubc-cs.github.io/cpsc340/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLWmXHcz_53Q02ZLeAxigki1JZFfCO6M-b) | 2018 | | 35. | **Foundations of Machine Learning** | David Rosenberg, Bloomberg | [FOML](https://bloomberg.github.io/foml/#home) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLnZuxOufsXnvftwTB1HL6mel1V32w0ThI) | 2018 | | 36. | **Introduction to Machine Learning** | Andreas Krause, ETH Zürich | [IntroML](https://las.inf.ethz.ch/teaching/introml-s18) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLzn6LN6WhlN273tsqyfdrBUsA-o5nUESV) | 2018 | | 37. | **Machine Learning Fundamentals** | Sanjoy Dasgupta, UC-San Diego | [MLF-slides](https://drive.google.com/drive/folders/1l1rwv-jMihLZIpW0zTgGN9-snWOsA3M9) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_onPhFCkVQhUzcTVgQiC8W2ShZKWlm0s) | 2018 | | 38. | **Machine Learning** | Jordan Boyd-Graber, University of Maryland | [CMSC-726](http://users.umiacs.umd.edu/~jbg/teaching/CMSC_726/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLegWUnz91WfsELyRcZ7d1GwAVifDaZmgo) | 2015-2018 | | 39. | **Machine Learning** | Andrew Ng, Stanford University | [CS229](http://cs229.stanford.edu/syllabus-autumn2018.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU) | 2018 | | 40. | **Machine Intelligence** | H.R.Tizhoosh, UWaterloo | [SYDE-522](https://kimialab.uwaterloo.ca/kimia/index.php/teaching/syde-522-machine-intelligence-2) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL4upCU5bnihwCX93Gv6AQnKmVMwx4AZoT) | 2019 | | | | | | | | | 41. | **Introduction to Machine Learning** | Pascal Poupart, University of Waterloo | [CS480/680](https://cs.uwaterloo.ca/~ppoupart/teaching/cs480-spring19) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdAoL1zKcqTW-uzoSVBNEecKHsnug_M0k) | S2019 | | 42. | **Advanced Machine Learning** | Thorsten Joachims, Cornell University | [CS-6780](https://www.cs.cornell.edu/courses/cs6780/2019sp) | [Lecture-Videos](https://cornell.mediasite.com/Mediasite/Catalog/Full/f5d1cd3323f746cca80b2468bf97efd421) | S2019 | | 43. | **Machine Learning for Structured Data** | Matt Gormley, Carnegie Mellon University | [10-418/10-618](http://www.cs.cmu.edu/~mgormley/courses/10418/schedule.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL4CxkUJbvNVihRKP4bXufvRLIWzeS-ieP) | F2019 | | 44. | **Advanced Machine Learning** | Joachim Buhmann, ETH Zürich | [ML2-AML](https://ml2.inf.ethz.ch/courses/aml/) | [Lecture-Videos](https://video.ethz.ch/lectures/d-infk/2019/autumn/252-0535-00L.html) | F2019 | | 45. | **Machine Learning for Signal Processing** | Vipul Arora, IIT-Kanpur | [MLSP](http://home.iitk.ac.in/~vipular/stuff/2019_MLSP.html) | [Lecture-Videos](https://iitk-my.sharepoint.com/:f:/g/personal/vipular_iitk_ac_in/Enf97NZfsoVBiyclC6yHfe4BlUv6CA4U8LPQQ4vtsDo_Xg) | F2019 | | 46. | **Foundations of Machine Learning** | Animashree Anandkumar, CalTech | [CMS-165](http://tensorlab.cms.caltech.edu/users/anima/cms165-2019.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLVNifWxslHCA5GUh0o92neMiWiQiGVFqp) | 2019 | | 47. | **Machine Learning for Physicists** | Florian Marquardt, Uni Erlangen-Nürnberg | `None` | [Lecture-Videos](https://www.video.uni-erlangen.de/course/id/778) | 2019 | | 48. | **Applied Machine Learning** | Andreas Müller, Columbia University | [COMS-W4995](https://www.cs.columbia.edu/~amueller/comsw4995s19/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_pVmAaAnxIQGzQS2oI3OWEPT-dpmwTfA) | 2019 | | 49. | **Fundamentals of Machine Learning over Networks** | Hossein Shokri-Ghadikolaei, KTH, Sweden | [MLoNs](https://sites.google.com/view/mlons/course-materials) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLWoZTd81WFCEBFrxDfNUrDnt3ABdLfg80) | 2019 | | 50. | **Foundations of Machine Learning and Statistical Inference** | Animashree Anandkumar, CalTech | [CMS-165](http://tensorlab.cms.caltech.edu/users/anima/cms165-2020.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLVNifWxslHCDlbyitaLLYBOAEPbmF1AHg) | 2020 | | | | | | | | | 51. | **Machine Learning** | Rebecca Willett and Yuxin Chen, University of Chicago | [STAT 37710 / CMSC 35400](https://voices.uchicago.edu/willett/teaching/stats37710-cmsc35400-s20) | [Lecture-Videos](https://voices.uchicago.edu/willett/teaching/stats37710-cmsc35400-s20) | S2020 | | 52. | **Introduction to Machine Learning** | Sanjay Lall and Stephen Boyd, Stanford University | [EE104/CME107](http://ee104.stanford.edu) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rN_Uy7_wmS051_q1d6akXmK) | S2020 | | 53. | **Applied Machine Learning** | Andreas Müller, Columbia University | [COMS-W4995](https://www.cs.columbia.edu/~amueller/comsw4995s20/schedule/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_pVmAaAnxIRnSw6wiCpSvshFyCREZmlM) | S2020 | | 54. | **Statistical Machine Learning** | Ulrike von Luxburg, Eberhard Karls Universität Tübingen | [Stat-ML](https://www.tml.cs.uni-tuebingen.de/teaching/2020_statistical_learning/index.php) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij2XCvrRzLokX6EoHWaGA2cC) | SS2020 | | 55. | **Probabilistic Machine Learning** | Philipp Hennig, Eberhard Karls Universität Tübingen | [Prob-ML](https://uni-tuebingen.de/en/180804) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij1tHaOFY96m5uX3J21a6yNd) | SS2020 | | 56. | **Machine Learning** | Sarath Chandar, PolyMTL, UdeM, Mila | [INF8953CE](http://sarathchandar.in/teaching/ml/fall2020) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLImtCgowF_ET0mi-AmmqQ0SIJUpWYaIOr) | F2020 | | 57. | **Machine Learning** | Erik Bekkers, Universiteit van Amsterdam | [UvA-ML](https://uvaml1.github.io/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL8FnQMH2k7jzhtVYbKmvrMyXDYMmgjj_n) | F2020 | | 58. | **Neural Networks for Signal Processing** | Shayan Srinivasa Garani, Indian Institute of Science | [NN4SP](https://labs.dese.iisc.ac.in/pnsil/neural-networks-and-learning-systems-i-fall-2020/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgMDNELGJ1CZn1399dV7_U4VBNJflRsua) | F2020 | | 59. | **Introduction to Machine Learning** | Dmitry Kobak, Universität Klinikum Tübingen | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij35ShKLDqccJSDntugY4FQT) | 2020 | | 60. | **Machine Learning (PRML)** | Erik J. Bekkers, Universiteit van Amsterdam | [UvAML-1](https://uvaml1.github.io) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL8FnQMH2k7jzhtVYbKmvrMyXDYMmgjj_n) | 2020 | | | | | | | | | 61. | **Machine Learning with Kernel Methods** | Julien Mairal and Jean-Philippe Vert, Inria/ENS Paris-Saclay, Google | [ML-Kernels](http://members.cbio.mines-paristech.fr/~jvert/svn/kernelcourse/course/2021mva/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLD93kGj6_EdrkNj27AZMecbRlQ1SMkp_o) | S2021 | | 62. | **Continual Learning** | Vincenzo Lomonaco, Università di Pisa | [ContLearn'21](https://course.continualai.org/background/details) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLm6QXeaB-XkBfM5RgQP6wCR7Jegdg51Px) | 2021 | | 63. | **Causality** | Christina Heinze-Deml, ETH Zurich | [Causal'21](https://stat.ethz.ch/lectures/ss21/causality.php#course_materials) | [YouTube-Lectures](https://stat.ethz.ch/lectures/ss21/causality.php#course_materials) | 2021 | | | | | | | | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ### :balloon: Reinforcement Learning :hotsprings: :video_game: :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: | S.No | Course Name | University/Instructor(s) | Course Webpage | Video Lectures | Year | | ---- | -------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------ | | 1. | **A Short Course on Reinforcement Learning** | Satinder Singh, UMichigan | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM4Pv4KYYzGy4cIFQ5C36-1jMNLab80Ky) | 2011 | | 2. | **Approximate Dynamic Programming** | Dimitri P. Bertsekas, MIT | [Lecture-Slides](http://adpthu2014.weebly.com/slides--materials.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLiCLbsFQNFAxOmVeqPhI5er1LGf2-L9I4) | 2014 | | 3. | **Introduction to Reinforcement Learning** | David Silver, DeepMind | [UCL-RL](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ) | 2015 | | 4. | **Reinforcement Learning** | Charles Isbell, Chris Pryby, GaTech; Michael Littman, Brown | [RL-Udacity](https://eu.udacity.com/course/reinforcement-learning--ud600) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLAwxTw4SYaPnidDwo9e2c7ixIsu_pdSNp) | 2015 | | 5. | **Reinforcement Learning** | Balaraman Ravindran, IIT Madras | [RL-IITM](https://www.cse.iitm.ac.in/~ravi/courses/Reinforcement%20Learning.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLNdWVHi37UggQIVcaZcmtGGEQHY9W7d9D) | 2016 | | 6. | **Deep Reinforcement Learning** | Sergey Levine, UC Berkeley | [CS-294](http://rail.eecs.berkeley.edu/deeprlcoursesp17/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLkFD6_40KJIwTmSbCv9OVJB3YaO4sFwkX) | S2017 | | 7. | **Deep Reinforcement Learning** | Sergey Levine, UC Berkeley | [CS-294](http://rail.eecs.berkeley.edu/deeprlcourse-fa17/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLkFD6_40KJIznC9CDbVTjAF2oyt8_VAe3) | F2017 | | 8. | **Deep RL Bootcamp** | Many legends, UC Berkeley | [Deep-RL](https://sites.google.com/view/deep-rl-bootcamp/lectures) | [YouTube-Lectures](https://www.youtube.com/channel/UCTgM-VlXKuylPrZ_YGAJHOw/videos) | 2017 | | 9 | **Data Efficient Reinforcement Learning** | Lots of Legends, Canary Islands | [DERL-17](http://dalimeeting.org/dali2017/data-efficient-reinforcement-learning.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL-tWvTpyd1VAvDpxukup6w-SuZQQ7e8K8) | 2017 | | 10. | **Deep Reinforcement Learning** | Sergey Levine, UC Berkeley | [CS-294-112](http://rail.eecs.berkeley.edu/deeprlcourse-fa18/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLkFD6_40KJIxJMR-j5A1mkxK26gh_qg37) | 2018 | | | | | | | | | 11. | **Reinforcement Learning** | Pascal Poupart, University of Waterloo | [CS-885](https://cs.uwaterloo.ca/~ppoupart/teaching/cs885-spring18/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdAoL1zKcqTXFJniO3Tqqn6xMBBL07EDc) | 2018 | | 12. | **Deep Reinforcement Learning and Control** | Katerina Fragkiadaki and Tom Mitchell, CMU | [10-703](http://www.andrew.cmu.edu/course/10-703/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLpIxOj-HnDsNfvOwRKLsUobmnF2J1l5oV) | 2018 | | 13. | **Reinforcement Learning and Optimal Control** | Dimitri Bertsekas, Arizona State University | [RLOC](http://web.mit.edu/dimitrib/www/RLbook.html) | [Lecture-Videos](http://web.mit.edu/dimitrib/www/RLbook.html) | 2019 | | 14. | **Reinforcement Learning** | Emma Brunskill, Stanford University | [CS 234](http://web.stanford.edu/class/cs234/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rOSOPzutgyCTapiGlY2Nd8u) | 2019 | | 15. | **Reinforcement Learning Day** | Lots of Legends, Microsoft Research, New York | [RLD-19](https://www.microsoft.com/en-us/research/event/reinforcement-learning-day-2019/#!agenda) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLD7HFcN7LXRe9nWEX3Up-RiCDi6-0mqVC) | 2019 | | 16. | **New Directions in Reinforcement Learning and Control** | Lots of Legends, IAS, Princeton University | [NDRLC-19](https://www.math.ias.edu/ndrlc) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdDZb3TwJPZ61sGqd6cbWCmTc275NrKu3) | 2019 | | 17. | **Deep Reinforcement Learning** | Sergey Levine, UC Berkeley | [CS 285](http://rail.eecs.berkeley.edu/deeprlcourse-fa19) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLkFD6_40KJIwhWJpGazJ9VSj9CFMkb79A) | F2019 | | 18. | **Deep Multi-Task and Meta Learning** | Chelsea Finn, Stanford University | [CS 330](https://cs330.stanford.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rMC6zfYmnD7UG3LVvwaITY5) | F2019 | | 19. | **RL-Theory Seminars** | Lots of Legends, Earth | [RL-theory-sem](https://sites.google.com/view/rltheoryseminars/past-seminars) | [YouTube-Lectures](https://www.youtube.com/channel/UCfBFutC9RbKK6p--B4R9ebA/videos) | 2020 - | | 20. | **Deep Reinforcement Learning** | Sergey Levine, UC Berkeley | [CS 285](http://rail.eecs.berkeley.edu/deeprlcourse-fa20) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_iWQOsE6TfURIIhCrlt-wj9ByIVpbfGc) | F2020 | | | | | | | | | 21. | **Introduction to Reinforcement Learning** | Amir-massoud Farahmand, Vector Institute, University of Toronto | [RL-intro](https://amfarahmand.github.io/IntroRL) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLCveiXxL2xNbiDq51a8iJwPRq2aO0ykrq) | S2021 | | 22. | **Reinforcement Learning** | Antonio Celani and Emanuele Panizon, International Centre for Theoretical Physics | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLp0hSY2uBeP8q2G3mfHGVGvQFEMX0QRWM) | 2021 | | 23. | **Computational Sensorimotor Learning** | Pulkit Agrawal, MIT-CSAIL | [6.884-CSL](https://pulkitag.github.io/6.884/lectures) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLwNwxAG-kBxPMTIs2fKWSsf7HqL2TcC78) | S2021 | | 24. | **Reinforcement Learning** | Dimitri P. Bertsekas, ASU/MIT | [RL-21](http://web.mit.edu/dimitrib/www/RLbook.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLmH30BG15SIp79JRJ-MVF12uvB1qPtPzn) | S2021 | | 25. | **Reinforcement Learning** | Sarath Chandar, École Polytechnique de Montréal | [INF8953DE](https://chandar-lab.github.io/INF8953DE) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLImtCgowF_ES_JdF_UcM60EXTcGZg67Ua) | F2021 | | 26. | **Deep Reinforcement Learning** | Sergey Levine, UC Berkeley | [CS 285](http://rail.eecs.berkeley.edu/deeprlcourse) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_iWQOsE6TfXxKgI1GgyV1B_Xa0DxE5eH) | F2021 | | 27. | **Reinforcement Learning Lecture Series** | Lots of Legends, DeepMind & UC London | [RL-series](https://deepmind.com/learning-resources/reinforcement-learning-series-2021) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm) | 2021 | | 28. | **Reinforcement Learning** | Dimitri P. Bertsekas, ASU/MIT | [RL-22](http://web.mit.edu/dimitrib/www/RLbook.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLmH30BG15SIoXhxLldoio0BhsIY84YMDj) | S2022 | | | | | | | | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ### :loudspeaker: Probabilistic Graphical Models :sparkles: :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: | S.No | Course Name | University/Instructor(s) | Course WebPage | Lecture Videos | Year | | ---- | ------------------------------------------------------------ | --------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------- | | 1. | **Probabilistic Graphical Models** | Many Legends, MPI-IS | [MLSS-Tuebingen](http://mlss.tuebingen.mpg.de/2013/2013/speakers.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLL0GjJzXhAWTRiW_ynFswMaiLSa0hjCZ3) | 2013 | | 2. | **Probabilistic Modeling and Machine Learning** | Zoubin Ghahramani, University of Cambridge | [WUST-Wroclaw](https://www.ii.pwr.edu.pl/~gonczarek/zoubin.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLwUOK5j_XOsdfVAGKErx9HqnrVZIuRbZ2) | 2013 | | 3. | **Probabilistic Graphical Models** | Eric Xing, CMU | [10-708](http://www.cs.cmu.edu/~epxing/Class/10708/lecture.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLI3nIOD-p5aoXrOzTd1P6CcLavu9rNtC-) | 2014 | | 4. | **Learning with Structured Data: An Introduction to Probabilistic Graphical Models** | Christoph Lampert, IST Austria | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLEqoHzpnmTfA0wc1JxjoVVOrJlx8W0rGf) | 2016 | | 5. | **Probabilistic Graphical Models** | Nicholas Zabaras, University of Notre Dame | [PGM](https://www.zabaras.com/probabilistic-graphical-models) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLd-PuDzW85AcV4bgdu7wHPL37hm60W4RM) | 2018 | | 6. | **Probabilistic Graphical Models** | Eric Xing, CMU | [10-708](https://sailinglab.github.io/pgm-spring-2019/) | [Lecture-Videos](https://sailinglab.github.io/pgm-spring-2019/lectures)
[YouTube-Lectures](https://www.youtube.com/playlist?list=PLoZgVqqHOumTY2CAQHL45tQp6kmDnDcqn) | S2019 | | 7. | **Probabilistic Graphical Models** | Eric Xing, CMU | [10-708](https://www.cs.cmu.edu/~epxing/Class/10708-20/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoZgVqqHOumTqxIhcdcpOAJOOimrRCGZn) | S2020 | | 8. | **Uncertainty Modeling in AI** | Gim Hee Lee, National University of Singapura (NUS) | [CS 5340 - CH](https://www.coursehero.com/sitemap/schools/2652-National-University-of-Singapore/courses/7821096-CS5340/), [CS 5340-NB](https://github.com/clear-nus/CS5340-notebooks) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLxg0CGqViygOb9Eyc8IXM27doxjp2SK0H) | 2020-21 | | | | | | | | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ## :game_die: Bayesian Deep Learning :spades: :gem: :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: | S.No | Course Name | University/Instructor(s) | Course WebPage | Lecture Videos | Year | | ---- | --------------------------------------------------- | --------------------------------- | -------------------------------------------------------- | ------------------------------------------------------------ | -------- | | 1. | **Bayesian Neural Networks, Variational Inference** | Lots of Legends | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM4Pv4KYYzGwUB4bFy183hwGhpL9ytvA1) | 2014-now | | 2. | **Variational Inference** | Chieh Wu, Northeastern University | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdk2fd27CQzSd1sQ3kBYL4vtv6GjXvPsE) | 2015 | | 3. | **Deep Learning and Bayesian Methods** | Lots of Legends, HSE Moscow | [DLBM-SS](http://deepbayes.ru/2018) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLe5rNUydzV9Q01vWCP9BV7NhJG3j7mz62) | 2018 | | 4. | **Deep Learning and Bayesian Methods** | Lots of Legends, HSE Moscow | [DLBM-SS](http://deepbayes.ru/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLe5rNUydzV9QHe8VDStpU0o8Yp63OecdW) | 2019 | | 5. | **Nordic Probabilistic AI** | Lots of Legends, NTNU, Trondheim | [ProbAI](https://github.com/probabilisticai/probai-2019) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLRy-VW__9hV8s--JkHXZvnd26KgjRP2ik) | 2019 | | | | | | | | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ## :movie_camera: Medical Imaging :camera: :video_camera: :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: | S.No | Course Name | University/Instructor(s) | Course WebPage | Lecture Videos | Year | | ---- | ------------------------------------------------------------ | ------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ----- | | 1. | **Medical Imaging Summer School** | Lots of Legends, Sicily | [MISS-14](http://iplab.dmi.unict.it/miss14/programme.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_VeUGLULXQtvcCdAgmvKoJ1k0Ajhz-Qu) | 2014 | | 2. | **Biomedical Image Analysis Summer School** | Lots of Legends, Paris | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgSHH6boFf5uJAUT4ZRiAZc_ofXolkAGK) | 2015 | | 3. | **Medical Imaging Summer School** | Lots of Legends, Sicily | [MISS-16](http://iplab.dmi.unict.it/miss16/programme.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTRCr47yTx5iXIYSneX3LKf16upaw59wa) | 2016 | | 4. | **OPtical and UltraSound imaging - OPUS** | Lots of Legends, Université de Lyon, France | [OPUS'16](https://opus2016lyon.sciencesconf.org/resource/page/id/2) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL95ayoVLX8GdUKbxu-R9WqRWwzdWcKjti) | 2016 | | 5. | **Medical Imaging Summer School** | Lots of Legends, Sicily | [MISS-18](http://iplab.dmi.unict.it/miss/programme.htm) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_VeUGLULXQux1dV4iA3XuMX6AueJmGGa) | 2018 | | 6. | **Seminar on AI in Healthcare** | Lots of Legends, Stanford | [CS 522](http://cs522.stanford.edu/2018/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLYn-ZmPR1DtNQJ-ot-L2V2EgUEH6OH_7w) | 2018 | | 7. | **Machine Learning for Healthcare** | David Sontag, Peter Szolovits, CSAIL MIT | [MLHC-19](https://mlhc19mit.github.io/)
[MIT 6.S897](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-s897-machine-learning-for-healthcare-spring-2019/lecture-notes/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLUl4u3cNGP60B0PQXVQyGNdCyCTDU1Q5j) | S2019 | | 8. | **Deep Learning and Medical Applications** | Lots of Legends, IPAM, UCLA | [DLM-20](https://www.ipam.ucla.edu/programs/workshops/deep-learning-and-medical-applications/?tab=schedule) | [Lecture-Videos](https://www.ipam.ucla.edu/programs/workshops/deep-learning-and-medical-applications/?tab=schedule) | 2020 | | 9. | **Stanford Symposium on Artificial Intelligence in Medicine and Imaging** | Lots of Legends, Stanford AIMI | [AIMI-20](https://aimi.stanford.edu/news-events/aimi-symposium/agenda) | [YouTube-Lectures](https://www.youtube.com/watch?v=tR2ObiL4il8&list=PLe6zdIMe5B7IR0oDOobXBDBlYY1eqLYPx) | 2020 | | | | | | | | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ## :tada: Graph Neural Networks (Geometric DL) :confetti_ball: :balloon: :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: | S.No | Course Name | University/Instructor(s) | Course WebPage | Lecture Videos | Year | | ---- | ------------------------------------------------------------ | ------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ----- | | 1. | **Deep learning on graphs and manifolds** | Michael Bronstein, Technion | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLH39kM3nuavcVOUIIBraBNHjv-CwEd1uV) | 2017 | | 2. | **Geometric Deep Learning on Graphs and Manifolds** | Michael Bronstein, Technische Universität München | `None` | [Lec-part1](https://streams.tum.de/Mediasite/Play/1f3b894e78f6400daa7885c886b936fb1d),
[Lec-part2](https://streams.tum.de/Mediasite/Play/6039c846b2f84e7a806024c06e3f5c5c1d) | 2017 | | 3. | **Eurographics Symposium on Geometry Processing - Graduate School** | Lots of Legends, SIGGRAPH, London | [SGP-2017](http://geometry.cs.ucl.ac.uk/SGP2017/?p=gradschool) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLOp-ngXvomHArqntgLVNzuJNdzNx3rDjZ) | 2017 | | 4. | **Eurographics Symposium on Geometry Processing - Graduate School** | Lots of Legends, SIGGRAPH, Paris | [SGP-2018](https://sgp2018.sciencesconf.org/resource/page/id/7) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLvcoRb-DvAmgpp8LYw7dUvLxh-1Vrrm-v) | 2018 | | 5. | **Analysis of Networks: Mining and Learning with Graphs** | Jure Leskovec, Stanford University | [CS224W](http://snap.stanford.edu/class/cs224w-2018/) | [Lecture-Videos](http://snap.stanford.edu/class/cs224w-2018/) | 2018 | | 6. | **Machine Learning with Graphs** | Jure Leskovec, Stanford University | [CS224W](http://snap.stanford.edu/class/cs224w-2019/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL-Y8zK4dwCrQyASidb2mjj_itW2-YYx6-) | 2019 | | 7. | Geometry and Learning from Data in 3D and Beyond -**Geometry and Learning from Data Tutorials** | Lots of Legends, IPAM UCLA | [GLDT](http://www.ipam.ucla.edu/programs/workshops/geometry-and-learning-from-data-tutorials) | [Lecture-Videos](http://www.ipam.ucla.edu/programs/workshops/geometry-and-learning-from-data-tutorials/?tab=schedule) | 2019 | | 8. | Geometry and Learning from Data in 3D and Beyond - **Geometric Processing** | Lots of Legends, IPAM UCLA | [GeoPro](http://www.ipam.ucla.edu/programs/workshops/workshop-i-geometric-processing/) | [Lecture-Videos](http://www.ipam.ucla.edu/programs/workshops/workshop-i-geometric-processing/?tab=schedule) | 2019 | | 9. | Geometry and Learning from Data in 3D and Beyond - **Shape Analysis** | Lots of Legends, IPAM UCLA | [Shape-Analysis](http://www.ipam.ucla.edu/programs/workshops/workshop-ii-shape-analysis/) | [Lecture-Videos](http://www.ipam.ucla.edu/programs/workshops/workshop-ii-shape-analysis/?tab=schedule) | 2019 | | 10. | Geometry and Learning from Data in 3D and Beyond - **Geometry of Big Data** | Lots of Legends, IPAM UCLA | [Geo-BData](http://www.ipam.ucla.edu/programs/workshops/workshop-iii-geometry-of-big-data) | [Lecture-Videos](http://www.ipam.ucla.edu/programs/workshops/workshop-iii-geometry-of-big-data/?tab=schedule) | 2019 | | | | | | | | | 11. | Geometry and Learning from Data in 3D and Beyond - **Deep Geometric Learning of Big Data and Applications** | Lots of Legends, IPAM UCLA | [DGL-BData](http://www.ipam.ucla.edu/programs/workshops/workshop-iv-deep-geometric-learning-of-big-data-and-applications) | [Lecture-Videos](http://www.ipam.ucla.edu/programs/workshops/workshop-iv-deep-geometric-learning-of-big-data-and-applications/?tab=schedule) | 2019 | | 12. | **Israeli Geometric Deep Learning** | Lots of Legends, Israel | [iGDL-20](https://gdl-israel.github.io/schedule.html) | [Lecture-Videos](https://www.youtube.com/watch?v=c8_32IVn-sg) | 2020 | | 13. | **Machine Learning for Graphs and Sequential Data** | Stephan Günnemann, Technische Universität München (TUM) | [MLGS-20](https://www.in.tum.de/en/daml/teaching/summer-term-2020/machine-learning-for-graphs-and-sequential-data/) | [Lecture-Videos](https://www.in.tum.de/daml/teaching/mlgs/) | S2020 | | 14. | **Machine Learning with Graphs** | Jure Leskovec, Stanford | [CS224W](http://web.stanford.edu/class/cs224w) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rPLKxIpqhjhPgdQy7imNkDn) | W2021 | | 15. | **Geometric Deep Learning** - AMMI | Lots of Legends, Virtual | [GDL-AMMI](https://geometricdeeplearning.com/lectures) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLn2-dEmQeTfQ8YVuHBOvAhUlnIPYxkeu3) | 2021 | | 16. | **Summer School on Geometric Deep Learning** - | Lots of Legends, DTU, DIKU & AAU | [GDL- DTU, DIKU & AAU](https://geometric-deep-learning.compute.dtu.dk) | [Lecture-Videos](https://geometric-deep-learning.compute.dtu.dk/talks-and-materials) | 2021 | | 17. | **Graph Neural Networks** | Alejandro Ribeiro, University of Pennsylvania | [ESE 514](https://gnn.seas.upenn.edu) | [YouTube-Lectures](https://www.youtube.com/channel/UC_YPrqpiEqkeGOG1TCt0giQ/playlists) | F2021 | | | | | | | | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ### :hibiscus: Natural Language Processing :cherry_blossom: :sparkling_heart: :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: | S.No | Course Name | University/Instructor(s) | Course WebPage | Lecture Videos | Year | | ---- | --------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | --------- | | 1. | **Computational Linguistics I** | Jordan Boyd-Graber, University of Maryland | [CMS-723](http://users.umiacs.umd.edu/~jbg/teaching/CMSC_723/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLegWUnz91WfuPebLI97-WueAP90JO-15i) | 2013-2018 | | 2. | **Deep Learning for Natural Language Processing** | Nils Reimers, TU Darmstadt | [DL4NLP](https://github.com/UKPLab/deeplearning4nlp-tutorial) | [YouTube-Lectures](https://www.youtube.com/channel/UC1zCuTrfpjT6Sv2kJk-JkvA/videos) | 2015-2017 | | 3. | **Deep Learning for Natural Language Processing** | Many Legends, DeepMind-Oxford | [DL-NLP](http://www.cs.ox.ac.uk/teaching/courses/2016-2017/dl/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL613dYIGMXoZBtZhbyiBqb0QtgK6oJbpm) | 2017 | | 4. | **Deep Learning for Speech & Language** | UPC Barcelona | [DL-SL](https://telecombcn-dl.github.io/2017-dlsl/) | [Lecture-Videos](https://telecombcn-dl.github.io/2017-dlsl/) | 2017 | | 5. | **Neural Networks for Natural Language Processing** | Graham Neubig, CMU | [NN4NLP](http://www.phontron.com/class/nn4nlp2017/) [Code](https://github.com/neubig/nn4nlp-code) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL8PYTP1V4I8ABXzdqtOpB_eqBlVAz_xPT) | 2017 | | 6. | **Neural Networks for Natural Language Processing** | Graham Neubig, CMU | [NN4-NLP](http://www.phontron.com/class/nn4nlp2018/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL8PYTP1V4I8Ba7-rY4FoB4-jfuJ7VDKEE) | 2018 | | 7. | **Deep Learning for NLP** | Min-Yen Kan, NUS | [CS-6101](https://www.comp.nus.edu.sg/~kanmy/courses/6101_1810/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLllwxvcS7ca5eD44KTCiT7Rmu_hFAafXB) | 2018 | | 8. | **Neural Networks for Natural Language Processing** | Graham Neubig, CMU | [NN4NLP](http://www.phontron.com/class/nn4nlp2019/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL8PYTP1V4I8Ajj7sY6sdtmjgkt7eo2VMs) | 2019 | | 9. | **Natural Language Processing with Deep Learning** | Abigail See, Chris Manning, Richard Socher, Stanford University | [CS224n](http://web.stanford.edu/class/cs224n/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z) | 2019 | | 10. | **Natural Language Understanding** | Bill MacCartney and Christopher Potts | [CS224U](https://web.stanford.edu/class/cs224u) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rObpMCir6rNNUlFAn56Js20) | S2019 | | | | | | | | | 11. | **Neural Networks for Natural Language Processing** | Graham Neubig, Carnegie Mellon University | [CS 11-747](http://www.phontron.com/class/nn4nlp2020/schedule.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL8PYTP1V4I8CJ7nMxMC8aXv8WqKYwj-aJ) | S2020 | | 12. | **Advanced Natural Language Processing** | Mohit Iyyer, UMass Amherst | [CS 685](https://people.cs.umass.edu/~miyyer/cs685) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLWnsVgP6CzadmQX6qevbar3_vDBioWHJL) | F2020 | | 13. | **Machine Translation** | Philipp Koehn, Johns Hopkins University | [EN 601.468/668](http://mt-class.org/jhu/syllabus.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLQrCiUDqDLG0lQX54o9jB4phJ-SLI6ZBQ) | F2020 | | 14. | **Neural Networks for NLP** | Graham Neubig, Carnegie Mellon University | [CS 11-747](http://www.phontron.com/class/nn4nlp2021) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL8PYTP1V4I8AkaHEJ7lOOrlex-pcxS-XV) | 2021 | | 15. | **Deep Learning for Natural Language Processing** | Kyunghyun Cho, New York University | [DS-GA 1011](https://drive.google.com/drive/folders/1ykXBtophaY_65VHK_8yDzZQJwfJDD5Ve) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdH9u0f1XKW_s-c8EcgJpn_HJz5Jj1IRf) | F2021 | | 16. | **Natural Language Processing with Deep Learning** | Chris Manning, Stanford University | [CS224n](https://web.stanford.edu/class/archive/cs/cs224n/cs224n.1214/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rOSH4v6133s9LFPRHjEmbmJ) | 2021 | | | | | | | | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ### :speaking_head: Automatic Speech Recognition :speech_balloon: :thought_balloon: :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: | S.No | Course Name | University/Instructor(s) | Course WebPage | Lecture Videos | Year | | ---- | ---------------------------------------- | ------------------------------ | --------------------------------------------------- | ------------------------------------------------------------ | --------- | | 1. | **Deep Learning for Speech & Language** | UPC Barcelona | [DL-SL](https://telecombcn-dl.github.io/2017-dlsl/) | [Lecture-Videos](https://telecombcn-dl.github.io/2017-dlsl/)
[YouTube-Videos](https://www.youtube.com/playlist?list=PL-5DCZHuHZkWeF9ljIjoC_X5gHRLNtIkU) | 2017 | | 2. | **Speech and Audio in the Northeast** | Many Legends, Google NYC | [SANE-15](http://www.saneworkshop.org/sane2015/) | [YouTube-Videos](https://www.youtube.com/playlist?list=PLBJWRPcgwk7sZOB4UTVilWWnRg84L9o5i) | 2015 | | 3. | **Automatic Speech Recognition** | Samudra Vijaya K, TIFR | `None` | [YouTube-Videos](https://www.youtube.com/channel/UCHk6uq1Cr9J3k5KNmIsYUNw/videos) | 2016 | | 4. | **Speech and Audio in the Northeast** | Many Legends, Google NYC | [SANE-17](http://www.saneworkshop.org/sane2017/) | [YouTube-Videos](https://www.youtube.com/playlist?list=PLBJWRPcgwk7tNLaBVu_S90ZQSblO3bwjg) | 2017 | | 5. | **Speech and Audio in the Northeast** | Many Legends, Google Cambridge | [SANE-18](http://www.saneworkshop.org/sane2018/) | [YouTube-Videos](https://www.youtube.com/playlist?list=PLBJWRPcgwk7sjMANn8jqosyHIMe6DJhmn) | 2018 | | | | | | | | | -1. | **Deep Learning for Speech Recognition** | Many Legends, AoE | `None` | [YouTube-Videos](https://www.youtube.com/playlist?list=PLM4Pv4KYYzGyFYCXV6YPWAKVOR2gmHnQd) | 2015-2018 | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ### :fire: Modern Computer Vision :camera_flash: :movie_camera: :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: | S.No | Course Name | University/Instructor(s) | Course WebPage | Lecture Videos | Year | | ---- | ------------------------------------------------------------ | ------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ---------- | | 1. | **Microsoft Computer Vision Summer School** - (classical) | Lots of Legends, Lomonosov Moscow State University | `None` | [YouTube-Videos](https://www.youtube.com/playlist?list=PLbwKcm5vdiSYU54xFUG1zoxQTulqvIcJu)
[Russian-mirror](https://www.youtube.com/playlist?list=PL-_cKNuVAYAUp0eCL7KO8QY4ETY3tIDFH) | 2011 | | 2. | **Computer Vision** - (classical) | Mubarak Shah, UCF | [CAP-5415](http://crcv.ucf.edu/courses/CAP5415/Fall2012/index.php) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLd3hlSJsX_Imk_BPmB_H3AQjFKZS9XgZm) | 2012 | | 3. | **Image and Multidimensional Signal Processing** - (classical) | William Hoff, Colorado School of Mines | [CSCI 510/EENG 510](http://inside.mines.edu/~whoff/courses/EENG510) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLyED3W677ALNv8Htn0f9Xh-AHe1aZPftv) | 2012 | | 4. | **Computer Vision** - (classical) | William Hoff, Colorado School of Mines | [CSCI 512/EENG 512](http://inside.mines.edu/~whoff/courses/EENG512/index.htm) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL4B3F8D4A5CAD8DA3) | 2012 | | 5. | **Image and Video Processing: From Mars to Hollywood with a Stop at the Hospital** | Guillermo Sapiro, Duke University | `None` | [YouTube-Videos](https://www.youtube.com/playlist?list=PLZ9qNFMHZ-A79y1StvUUqgyL-O0fZh2rs) | 2013 | | 6. | **Multiple View Geometry** (classical) | Daniel Cremers, Technische Universität München | [mvg](https://vision.in.tum.de/teaching/ss2014/mvg2014) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTBdjV_4f-EJn6udZ34tht9EVIW7lbeo4) | 2013 | | 7. | **Mathematical Methods for Robotics, Vision, and Graphics** | Justin Solomon, Stanford University | [CS-205A](http://graphics.stanford.edu/courses/cs205a/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLQ3UicqQtfNvQ_VzflHYKhAqZiTxOkSwi) | 2013 | | 8. | **Computer Vision** - (classical) | Mubarak Shah, UCF | [CAP-5415](http://crcv.ucf.edu/courses/CAP5415/Fall2014/index.php) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLd3hlSJsX_ImKP68wfKZJVIPTd8Ie5u-9) | 2014 | | 9. | **Computer Vision for Visual Effects** (classical) | Rich Radke, Rensselaer Polytechnic Institute | [ECSE-6969](https://www.ecse.rpi.edu/~rjradke/cvfxcourse.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLuh62Q4Sv7BUJlKlt84HFqSWfW36MDd5a) | S2014 | | 10. | **Autonomous Navigation for Flying Robots** | Juergen Sturm, Technische Universität München | [Autonavx](https://jsturm.de/wp/teaching/autonavx-slides/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTBdjV_4f-EKBCUs1HmMtsnXv4JUoFrzg) | 2014 | | | | | | | | | 11. | **SLAM - Mobile Robotics** | Cyrill Stachniss, Universitaet Freiburg | [RobotMapping](http://ais.informatik.uni-freiburg.de/teaching/ws13/mapping/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgnQpQtFTOGQrZ4O5QzbIHgl3b1JHimN_) | 2014 | | 12. | **Computational Photography** | Irfan Essa, David Joyner, Arpan Chakraborty | [CP-Udacity](https://eu.udacity.com/course/computational-photography--ud955) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLAwxTw4SYaPn-unAWtRMleY4peSe4OzIY) | 2015 | | 13. | **Introduction to Digital Image Processing** | Rich Radke, Rensselaer Polytechnic Institute | [ECSE-4540](https://www.ecse.rpi.edu/~rjradke/improccourse.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLuh62Q4Sv7BUf60vkjePfcOQc8sHxmnDX) | S2015 | | 14. | **Lectures on Digital Photography** | Marc Levoy, Stanford/Google Research | [LoDP](https://sites.google.com/site/marclevoylectures/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL7ddpXYvFXspUN0N-gObF1GXoCA-DA-7i) | 2016 | | 15. | **Introduction to Computer Vision** (foundation) | Aaron Bobick, Irfan Essa, Arpan Chakraborty | [CV-Udacity](https://eu.udacity.com/course/introduction-to-computer-vision--ud810) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLAwxTw4SYaPnbDacyrK_kB_RUkuxQBlCm) | 2016 | | 16. | **Computer Vision** | Syed Afaq Ali Shah, University of Western Australia | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLvqB6_mDBCdlnT84LK_NvbOqcXLlOTR8j) | 2016 | | 17. | **Photogrammetry I & II** | Cyrill Stachniss, University of Bonn | [PG-I&II](https://www.ipb.uni-bonn.de/photogrammetry-i-ii/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgnQpQtFTOGRsi5vzy9PiQpNWHjq-bKN1) | 2016 | | 18. | **Deep Learning for Computer Vision** | UPC Barcelona | [DLCV-16](http://imatge-upc.github.io/telecombcn-2016-dlcv/)
[DLCV-17](https://telecombcn-dl.github.io/2017-dlcv/)
[DLCV-18](https://telecombcn-dl.github.io/2018-dlcv/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL-5eMc3HQTBbuaTFP4wsfD2Y2VqEfQcaP) | 2016-2018 | | 19. | **Convolutional Neural Networks** | Andrew Ng, Stanford University | [DeepLearning.AI](https://www.deeplearning.ai/deep-learning-specialization/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLkDaE6sCZn6Gl29AoE31iwdVwSG-KnDzF) | 2017 | | 20. | **Variational Methods for Computer Vision** | Daniel Cremers, Technische Universität München | [VMCV](https://vision.in.tum.de/teaching/ws2016/vmcv2016) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTBdjV_4f-EJ7A2iIH5L5ztqqrWYjP2RI) | 2017 | | | | | | | | | 21. | **Winter School on Computer Vision** | Lots of Legends, Israel Institute for Advanced Studies | [WS-CV](http://www.as.huji.ac.il/cse) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTn74Qx5mPsSniA5tt6W-o0OGYEeKScug) | 2017 | | 22. | **Deep Learning for Visual Computing** | Debdoot Sheet, IIT-Kgp | [Nptel](https://onlinecourses.nptel.ac.in/noc18_ee08/preview) [Notebooks](https://github.com/iitkliv/dlvcnptel) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLuv3GM6-gsE1Biyakccxb3FAn4wBLyfWf) | 2018 | | 23. | **The Ancient Secrets of Computer Vision** | Joseph Redmon, Ali Farhadi | [TASCV](https://pjreddie.com/courses/computer-vision/) ; [TASCV-UW](https://courses.cs.washington.edu/courses/cse455/18sp/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLjMXczUzEYcHvw5YYSU92WrY8IwhTuq7p) | 2018 | | 24. | **Modern Robotics** | Kevin Lynch, Northwestern Robotics | [modern-robot](http://hades.mech.northwestern.edu/index.php/Modern_Robotics) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLggLP4f-rq02vX0OQQ5vrCxbJrzamYDfx) | 2018 | | 25. | **Digial Image Processing** | Alex Bronstein, Technion | [CS236860](https://vistalab-technion.github.io/cs236860/info/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM0a6Z788YAZOxUyWda9y3N_i2upIj1Ep) | 2018 | | 26. | **Mathematics of Imaging** - Variational Methods and Optimization in Imaging | Lots of Legends, Institut Henri Poincaré | [Workshop-1](http://www.ihp.fr/sites/default/files/conf1-04_au_08_fevr-imaging2019.pdf) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL9kd4mpdvWcAzD5Aq-P1TrLLiYckrloxw) | 2019 | | 27. | **Deep Learning for Video** | Xavier Giró, UPC Barcelona | [deepvideo](https://mcv-m6-video.github.io/deepvideo-2019/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL-5eMc3HQTBbPY-627Gornj09pZrNQgPD) | 2019 | | 28. | **Statistical modeling for shapes and imaging** | Lots of Legends, Institut Henri Poincaré, Paris | [workshop-2](https://imaging-in-paris.github.io/semester2019/workshop2prog) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL9kd4mpdvWcAzD5Aq-P1TrLLiYckrloxw) | 2019 | | 29. | **Imaging and machine learning** | Lots of Legends, Institut Henri Poincaré, Paris | [workshop-3](https://imaging-in-paris.github.io/semester2019/workshop3prog) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL9kd4mpdvWcAzD5Aq-P1TrLLiYckrloxw) | 2019 | | 30. | **Computer Vision** | Jayanta Mukhopadhyay, IIT Kgp | [CV-nptel](https://nptel.ac.in/courses/106/105/106105216/) | [YouTube-Lectures](https://nptel.ac.in/courses/106/105/106105216/) | 2019 | | | | | | | | | 31. | **Deep Learning for Computer Vision** | Justin Johnson, UMichigan | [EECS 498-007](https://web.eecs.umich.edu/~justincj/teaching/eecs498/) | [Lecture-Videos](http://leccap.engin.umich.edu/leccap/site/jhygcph151x25gjj1f0)
[YouTube-Lectures](https://www.youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r) | 2019 | | 32. | **Sensors and State Estimation 2** | Cyrill Stachniss, University of Bonn | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgnQpQtFTOGQh_J16IMwDlji18SWQ2PZ6) | S2020 | | 33. | **Computer Vision III: Detection, Segmentation and Tracking** | Laura Leal-Taixé, TU München | [CV3DST](https://dvl.in.tum.de/teaching/cv3dst-ss20/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLog3nOPCjKBneGyffEktlXXMfv1OtKmCs) | S2020 | | 34. | **Advanced Deep Learning for Computer Vision** | Laura Leal-Taixé and Matthias Nießner, TU München | [ADL4CV](https://dvl.in.tum.de/teaching/adl4cv-ss20) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLog3nOPCjKBnjhuHMIXu4ISE4Z4f2jm39) | S2020 | | 35. | **Computer Vision: Foundations** | Fred Hamprecht, Universität Heidelberg | [CVF](https://hci.iwr.uni-heidelberg.de/ial/cvf) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLuRaSnb3n4kRAbnmiyGd77hyoGzO9wPde) | SS2020 | | 36. | **MIT Vision Seminar** | Lots of Legends, MIT | [MIT-Vision](https://sites.google.com/view/visionseminar/past-talks) | [YouTube-Lectures](https://www.youtube.com/channel/UCLMiFkFyfcNnZs6iwYLPI9g/videos) | 2015-now | | 37. | **TUM AI Guest Lectures** | Lots of Legends, Technische Universität München | [TUM-AI](https://niessner.github.io/TUM-AI-Lecture-Series) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLQ8Y4kIIbzy8kMlz7cRqz-BjbdyWsfLXt) | 2020 - now | | 38. | **Seminar on 3D Geometry & Vision** | Lots of Legends, Virtual | [3DGV seminar](https://3dgv.github.io) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZk0jtN0g8e-xVTfsiV67q8Iz1cZO_FpV) | 2020 - now | | 39. | **Event-based Robot Vision** | Guillermo Gallego, Technische Universität Berlin | [EVIS-SS20](https://sites.google.com/view/guillermogallego/teaching/event-based-robot-vision) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL03Gm3nZjVgUFYUh3v5x8jVonjrGfcal8) | 2020 - now | | 40. | **Deep Learning for Computer Vision** | Vineeth Balasubramanian, IIT Hyderabad | [DL-CV'20](https://onlinecourses.nptel.ac.in/noc20_cs88/preview) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLyqSpQzTE6M_PI-rIz4O1jEgffhJU9GgG) | 2020 | | | | | | | | | 41. | **Deep Learning for Visual Computing** | Peter Wonka, KAUST, SA | `NOne` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLMpQLEui13s2DHbw6kTTxwQma8rehlfZE) | 2020 | | 42. | **Computer Vision** | Yogesh Rawat, University of Central Florida | [CAP5415-CV](https://www.crcv.ucf.edu/courses/cap5415-fall-2020/schedule/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLd3hlSJsX_Ikm5il1HgmDB_z62BeoikFX) | F2020 | | 43. | **Multimedia Signal Processing** | Mark Hasegawa-Johnson, UIUC | [ECE-417 MSP](https://courses.engr.illinois.edu/ece417/fa2020/) | [Lecture Videos](https://mediaspace.illinois.edu/channel/ECE%20417/26816181) | F2020 | | 44. | **Computer Vision** | Andreas Geiger, Universität Tübingen | [Comp.Vis](https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/autonomous-vision/lectures/computer-vision/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij35L2MHGzis8AEHz7mg381_) | S2021 | | 45. | **3D Computer Vision** | Lee Gim Hee, National Univeristy of Singapura | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLxg0CGqViygP47ERvqHw_v7FVnUovJeaz) | 2021 | | 46. | **Deep Learning for Computer Vision: Fundamentals and Applications** | T. Dekel et al., Weizmann Institute of Science | [DL4CV](https://dl4cv.github.io/schedule.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_Z2_U9MIJdNgFM7-f2fZ9ZxjVRP_jhJv) | S2021 | | 47. | **Current Topics in ML Methods in 3D and Geometric Deep Learning** | Animesh Garg & others, University of Toronto | [CSC 2547](http://www.pair.toronto.edu/csc2547-w21) | [YouTube-Lectures](https://www.youtube.com/channel/UCrsmAXnwu6sgccWevW12Dfg/videos) | 2021 | | 48. | **First Principles of Computer Vision** | Shree K. Nayar, Columbia University | [FPCV](https://fpcv.cs.columbia.edu) | [YouTube-Lectures](https://www.youtube.com/channel/UCf0WB91t8Ky6AuYcQV0CcLw/videos) | 2021 | | 49. | **Self-Driving Cars** | Andreas Geiger, Universität Tübingen | [SDC'21](https://uni-tuebingen.de/de/123611) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij321zzKXK6XCQXAaaYjQbzr) | W2021 | | | | | | | | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ### :star2: Boot Camps or Summer Schools :maple_leaf: :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: | S.No | Course Name | University/Instructor(s) | Course WebPage | Lecture Videos | Year | | ---- | ------------------------------------------------------- | -------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | --------- | | 1. | **Deep Learning, Feature Learning** | Lots of Legends, IPAM UCLA | [GSS-2012](https://www.ipam.ucla.edu/programs/summer-schools/graduate-summer-school-deep-learning-feature-learning/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLHyI3Fbmv0SdzMHAy0aN59oYnLy5vyyTA) | 2012 | | 2. | **Big Data Boot Camp** | Lots of Legends, Simons Institute | [Big Data](https://simons.berkeley.edu/workshops/schedule/316) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre13RmUC2AybRvVAxO5DEMIBH) | 2013 | | 3. | **Machine Learning Summer School** | Lots of Legends, MPI-IS Tübingen | [MLSS-13](http://mlss.tuebingen.mpg.de/2013/2013/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLqJm7Rc5-EXFv6RXaPZzzlzo93Hl0v91E) | 2013 | | 4 | **Graduate Summer School: Computer Vision** | Lots of Legends, IPAM-UCLA | [GSS-CV](http://www.ipam.ucla.edu/programs/summer-schools/graduate-summer-school-computer-vision/) | [Video-Lectures](http://www.ipam.ucla.edu/programs/summer-schools/graduate-summer-school-computer-vision/?tab=schedule) | 2013 | | 5. | **Machine Learning Summer School** | Lots of Legends, Reykjavik University | [MLSS-14](http://mlss2014.hiit.fi/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLqdbxUnkqOw2nKn7VxYqIrKWcqRkQYOsF) | 2014 | | 6. | **Machine Learning Summer School** | Lots of Legends, Pittsburgh | [MLSS-14](http://www.mlss2014.com) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZSO_6-bSqHQCIYxE3ycGLXHMjK3XV7Iz) | 2014 | | 7. | **Deep Learning Summer School** | Lots of Legends, Université de Montréal | [DLSS-15](https://sites.google.com/site/deeplearningsummerschool/home) | [YouTube-Lectures](http://videolectures.net/deeplearning2015_montreal/) | 2015 | | 8. | **Biomedical Image Analysis Summer School** | Lots of Legends, CentraleSupelec, Paris | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgSHH6boFf5uJAUT4ZRiAZc_ofXolkAGK) | 2015 | | 9. | **Mathematics of Signal Processing** | Lots of Legends, Hausdorff Institute for Mathematics | [SigProc](http://www.him.uni-bonn.de/signal-processing-2016/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLul8LCT3AJqSQo3lr5RbwxJ92RsgRuDtx) | 2016 | | 10. | **Microsoft Research - Machine Learning Course** | S V N Vishwanathan and Prateek Jain MS-Research | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL34iyE0uXtxo7vPXGFkmm6KbgZQwjf9Kf) | 2016 | | | | | | | | | 11. | **Deep Learning Summer School** | Lots of Legends, Université de Montréal | [DL-SS-16](https://sites.google.com/site/deeplearningsummerschool2016/home) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL5bqIc6XopCbb-FvnHmD1neVlQKwGzQyR) | 2016 | | 12. | **Lisbon Machine Learning School** | Lots of Legends, Instituto Superior Técnico, Portugal | [LxMLS-16](http://lxmls.it.pt/2016/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLToLj8M4ao-fymxXBIOU6sF1NGFLb5EiX) | 2016 | | 13. | **Machine Learning Advances and Applications Seminar** | Lots of Legends, Fields Institute, University of Toronto | [MLAAS-16](http://www.fields.utoronto.ca/activities/16-17/machine-learning) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLfsVAYSMwskuQcRkuDApP40lX_i08d0QK)
[Video-Lectures](http://www.fields.utoronto.ca/video-archive/event/2267) | 2016-2017 | | 14. | **Machine Learning Advances and Applications Seminar** | Lots of Legends, Fields Institute, University of Toronto | [MLAAS-17](http://www.fields.utoronto.ca/activities/17-18/machine-learning) | [Video Lectures](http://www.fields.utoronto.ca/video-archive/event/2487) | 2017-2018 | | 15. | **Machine Learning Summer School** | Lots of Legends, MPI-IS Tübingen | [MLSS-17](http://mlss.tuebingen.mpg.de/2017/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLqJm7Rc5-EXFUOvoYCdKikfck8YeUCnl9) | 2017 | | 16. | **Representation Learning** | Lots of Legends, Simons Institute | [RepLearn](https://simons.berkeley.edu/workshops/abstracts/3750) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre13UNV4ztsWUXciUZ7x_ZDHz) | 2017 | | 17. | **Foundations of Machine Learning** | Lots of Legends, Simons Institute | [ML-BootCamp](https://simons.berkeley.edu/workshops/abstracts/3748) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre11GbZWneln-VZDLHyejO7YD) | 2017 | | 18. | **Optimization, Statistics, and Uncertainty** | Lots of Legends, Simons Institute | [Optim-Stats](https://simons.berkeley.edu/workshops/abstracts/4795) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre13ACD44z2FH-IVP1e8ip5JO) | 2017 | | 19. | **Deep Learning: Theory, Algorithms, and Applications** | Lots of Legends, TU-Berlin | [DL: TAA](http://doc.ml.tu-berlin.de/dlworkshop2017/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLJOzdkh8T5kqCNV_v1w2tapvtJDZYiohW) | 2017 | | 20. | **Deep Learning and Reinforcement Learning Summer School** | Lots of Legends, Université de Montréal | [DLRL-2017](https://mila.quebec/en/cours/deep-learning-summer-school-2017/) | [Lecture-videos](http://videolectures.net/deeplearning2017_montreal/) | 2017 | | | | | | | | | 21. | **Statistical Physics Methods in Machine Learning** | Lots of Legends, International Centre for Theoretical Sciences, TIFR | [SPMML](https://www.icts.res.in/discussion-meeting/SPMML2017) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL04QVxpjcnjhtL3IIVyFRMOgdhWtPn7YJ) | 2017 | | 22. | **Lisbon Machine Learning School** | Lots of Legends, Instituto Superior Técnico, Portugal | [LxMLS-17](http://lxmls.it.pt/2017/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLToLj8M4ao-fuRfnzEJCCnvuW2_FeJ73N) | 2017 | | 23. | **Interactive Learning** | Lots of Legends, Simons Institute, Berkeley | [IL-2017](https://simons.berkeley.edu/workshops/schedule/3749) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre10T2POF-WzXh0ckdpyvANUx) | 2017 | | 24. | **Computational Challenges in Machine Learning** | Lots of Legends, Simons Institute, Berkeley | [CCML-17](https://simons.berkeley.edu/workshops/schedule/3751) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre12eXz4dnvc8oervo2_Af4iU) | 2017 | | 25. | **Foundations of Data Science** | Lots of Legends, Simons Institute | [DS-BootCamp](https://simons.berkeley.edu/workshops/abstracts/6680) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre13r1Qrnrejj3f498-NurSf3) | 2018 | | 26. | **Deep Learning and Bayesian Methods** | Lots of Legends, HSE Moscow | [DLBM-SS](http://deepbayes.ru/2018/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLe5rNUydzV9Q01vWCP9BV7NhJG3j7mz62) | 2018 | | 27. | **New Deep Learning Techniques** | Lots of Legends, IPAM UCLA | [IPAM-Workshop](https://www.ipam.ucla.edu/programs/workshops/new-deep-learning-techniques/?tab=schedule) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLHyI3Fbmv0SdM0zXj31HWjG9t9Q0v2xYN) | 2018 | | 28. | **Deep Learning and Reinforcement Learning Summer School** | Lots of Legends, University of Toronto | [DLRL-2018](https://dlrlsummerschool.ca/2018-event/) | [Lecture-videos](http://videolectures.net/DLRLsummerschool2018_toronto/) | 2018 | | 29. | **Machine Learning Summer School** | Lots of Legends, Universidad Autónoma de Madrid, Spain | [MLSS-18](http://mlss.ii.uam.es/mlss2018/index.html) | [YouTube-Lectures](https://www.youtube.com/channel/UCbPJHr__eIor_7jFH3HmVHQ/videos)
[Course-videos](http://mlss.ii.uam.es/mlss2018/speakers.html) | 2018 | | 30. | **Theoretical Basis of Machine Learning** | Lots of Legends, International Centre for Theoretical Sciences, TIFR | [TBML-18](https://www.icts.res.in/discussion-meeting/tbml2018) | [Lecture-Videos](https://www.icts.res.in/discussion-meeting/tbml2018/talks)
[YouTube-Videos](https://www.youtube.com/playlist?list=PL04QVxpjcnjj1DgnXxFBo2fkSju4r-ggr) | 2018 | | | | | | | | | 31. | **Polish View on Machine Learning** | Lots of Legends, Warsaw | [PLinML-18](https://plinml.mimuw.edu.pl/) | [YouTube-Videos](https://www.youtube.com/playlist?list=PLoaWrlj9TDhPcA6N9dZQ6GPXboYuumDRp) | 2018 | | 32. | **Big Data Analysis in Astronomy** | Lots of Legends, Tenerife | [BDAA-18](http://research.iac.es/winterschool/2018/pages/book-ws2018.php) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM4Pv4KYYzGx42W5pSp3Itetp0u-PENtI) | 2018 | | 33. | **Machine Learning Advances and Applications Seminar** | Lots of Legends, Fields Institute, University of Toronto | [MLASS](http://www.fields.utoronto.ca/activities/18-19/machine-learning) | [Video Lectures](http://www.fields.utoronto.ca/video-archive/event/2681) | 2018-2019 | | 34. | **MIFODS- ML, Stats, ToC seminar** | Lots of Legends, MIT | [MIFODS-seminar](http://mifods.mit.edu/seminar.php) | [Lecture-videos](http://mifods.mit.edu/seminar.php) | 2018-2019 | | 35. | **Learning Machines Seminar Series** | Lots of Legends, Cornell Tech | [LMSS](https://lmss.tech.cornell.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLycW2Yy79JuxbQZ9uHEu_NS3cGNomhL2A) | 2018-now | | 36. | **Machine Learning Summer School** | Lots of Legends, South Africa | [MLSS'19](https://mlssafrica.com/programme-schedule/) | [YouTube-Lectures](https://www.youtube.com/channel/UC722CmQVgcLtxt_jXr3RyWg/videos) | 2019 | | 37. | **Deep Learning Boot Camp** | Lots of Legends, Simons Institute, Berkeley | [DLBC-19](https://simons.berkeley.edu/workshops/schedule/10624) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre12c2Il9mNX0Cmp9Z4oFNrQh) | 2019 | | 38. | **Frontiers of Deep Learning** | Lots of Legends, Simons Institute, Berkeley | [FoDL-19](https://simons.berkeley.edu/workshops/schedule/10627) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre11ekU7g-Z_qsvjDD8cT-hi9) | 2019 | | 39. | **Mathematics of data: Structured representations for sensing, approximation and learning** | Lots of Legends, The Alan Turing Institute, London | [MoD-19](https://www.turing.ac.uk/sites/default/files/2019-05/agenda_9_3.pdf) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLuD_SqLtxSdX_w1Ztexpzl_EJgFQSkWez) | 2019 | | 40. | **Deep Learning and Bayesian Methods** | Lots of Legends, HSE Moscow | [DLBM-SS](http://deepbayes.ru/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLe5rNUydzV9QHe8VDStpU0o8Yp63OecdW) | 2019 | | | | | | | | | 41. | **The Mathematics of Deep Learning and Data Science** | Lots of Legends, Isaac Newton Institute, Cambridge | [MoDL-DS](https://gateway.newton.ac.uk/event/ofbw46) | [Lecture-Videos](https://gateway.newton.ac.uk/event/ofbw46/programme) | 2019 | | 42. | **Geometry of Deep Learning** | Lots of Legends, MSR Redmond | [GoDL](https://www.microsoft.com/en-us/research/event/ai-institute-2019) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLD7HFcN7LXRe30qq36It2XCljxc340O_d) | 2019 | | 43. | **Deep Learning for Science School** | Many folks, LBNL, Berkeley | [DLfSS](https://dl4sci-school.lbl.gov/agenda) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL20S5EeApOSvfvEyhCPOUzU7zkBcR5-eL) | 2019 | | 44. | **Emerging Challenges in Deep Learning** | Lots of Legends, Simons Institute, Berkeley | [ECDL](https://simons.berkeley.edu/workshops/schedule/10629) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre10BpafDrv0fg2VNUweWXWVd) | 2019 | | 45. | **Full Stack Deep Learning** | Pieter Abbeel and many others, UC Berkeley | [FSDL-M19](https://fullstackdeeplearning.com/march2019) | [YouTube-Lectures-Day-1](https://www.youtube.com/playlist?list=PL1T8fO7ArWlcf3Hc4VMEVBlH8HZm_NbeB)
[Day-2](https://www.youtube.com/playlist?list=PL1T8fO7ArWlf6TWwdstb-PcwlubnlrKrm) | 2019 | | 46. | **Algorithmic and Theoretical aspects of Machine Learning** | Lots of legends, IIIT-Bengaluru | [ACM-ML](https://india.acm.org/education/machine-learning)
[nptel](https://nptel.ac.in/courses/128/106/128106011/) | [YouTube-Lectures](https://nptel.ac.in/courses/128/106/128106011) | 2019 | | 47. | **Deep Learning and Reinforcement Learning Summer School** | Lots of Legends, AMII, Edmonton, Canada | [DLRL-2019](https://dlrlsummerschool.ca/past-years) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLKlhhkvvU8-aXmPQZNYG_e-2nTd0tJE8v) | 2019 | | 48. | **Mathematics of Machine Learning** - Summer Graduate School | Lots of Legends, University of Washington | [MoML-SGS](http://www.msri.org/summer_schools/866#schedule), [MoML-SS](http://mathofml.cs.washington.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTPQEx-31JXhguCush5J7OGnEORofoCW9) | 2019 | | 49. | **Workshop on Theory of Deep Learning: Where next?** | Lots of Legends, IAS, Princeton University | [WTDL](https://www.math.ias.edu/wtdl) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdDZb3TwJPZ5dqqg_S-rgJqSFeH4DQqFQ) | 2019 | | 50. | **Computational Vision Summer School** | Lots of Legends, Black Forest, Germany | [CVSS-2019](http://orga.cvss.cc/program-cvss-2019/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLeCNfJWZKqxsvidOlVLtWq9s7sIsX1QTC) | 2019 | | | | | | | | | 51. | **Learning under complex structure** | Lots of Legends, MIT | [LUCS](https://mifods.mit.edu/complex.php) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM4Pv4KYYzGwhIHcaY6zYR7M9hhFO4Vud) | 2020 | | 52. | **Machine Learning Summer School** | Lots of Legends, MPI-IS Tübingen (virtual) | [MLSS](http://mlss.tuebingen.mpg.de/2020/schedule.html) | [YouTube-Lectures](https://www.youtube.com/channel/UCBOgpkDhQuYeVVjuzS5Wtxw/videos) | SS2020 | | 53. | **Eastern European Machine Learning Summer School** | Lots of Legends, Kraków, Poland (virtual) | [EEML](https://www.eeml.eu/program) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLaKY4p4V3gE1j01FOY2FeglV4jRntQj84) | S2020 | | 54. | **Lisbon Machine Learning Summer School** | Lots of Legends, Lisbon, Portugal (virtual) | [LxMLS](http://lxmls.it.pt/2020/?page_id=19) | [YouTube-Lectures](https://www.youtube.com/channel/UCkVFZWgT1jR75UvSLGP9_mw) | S2020 | | 55. | **Workshop on New Directions in Optimization, Statistics and Machine Learning** | Lots of Legends, Institute of Advanced Study, Princeton | [ML-Opt new dir.](https://www.ias.edu/video/workshop/2020/0415-16) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdDZb3TwJPZ4Ri6i0MIdesIEpYK4lx17Q) | 2020 | | 56. | **Mediterranean Machine Learning School** | Lots of Legends, Italy (virtual) | [M2L-school](https://www.m2lschool.org/talks) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLF-wkqRv4u1YRbfnwN8cXXyrmXld-sked) | 2021 | | 57. | **Mathematics of Machine Learning - One World Seminar** | Lots of Legends, Virtual | [1W-ML](https://sites.google.com/view/oneworldml/past-events) | [YouTube-Lectures](https://www.youtube.com/channel/UCz7WlgXs20CzugkfxhFCNFg/videos) | 2020 - now | | 58. | **Deep Learning Theory Summer School** | Lots of Legends, Princeton University (virtual) | [DLT'21](https://deep-learning-summer-school.princeton.edu) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL2mB9GGlueJj_FNjJ8RWgz4Nc_hCSXfMU) | 2021 | | | | | | | | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ### :bird: Bird's Eye view of A(G)I :eagle: :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: | S.No | Course Name | University/Instructor(s) | Course WebPage | Lecture Videos | Year | | ---- | -------------------------------------- | -------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | --------- | | 1. | **Artificial General Intelligence** | Lots of Legends, MIT | [6.S099-AGI](https://agi.mit.edu/) | [Lecture-Videos](https://agi.mit.edu/) | 2018-2019 | | 2. | **AI Podcast** | Lots of Legends, MIT | [AI-Pod](https://lexfridman.com/ai/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4) | 2018-2019 | | 3. | **NYU - AI Seminars** | Lots of Legends, NYU | [modern-AI](https://engineering.nyu.edu/academics/departments/electrical-and-computer-engineering/ece-seminar-series/modern-artificial) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLhwo5ntex8iY9xhpSwWas451NgVuqBE7U) | 2017-now | | 4. | **Deep Learning: Alchemy or Science?** | Lots of Legends, Institute for Advanced Study, Princeton | [DLAS](https://video.ias.edu/deeplearning/2019/0222)
[Agenda](https://www.math.ias.edu/tml/dlasagenda) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdDZb3TwJPZ7aAxhIHALBoh8l6-UxmMNP) | 2019 | | | | | | | | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ### To-Do :running: :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: :white_large_square: Optimization courses which form the foundation for ML, DL, RL :white_large_square: Computer Vision courses which are DL & ML heavy :white_large_square: Speech recognition courses which are DL heavy :white_large_square: Structured Courses on Geometric, Graph Neural Networks :white_large_square: Section on Autonomous Vehicles :white_large_square: Section on Computer Graphics with ML/DL focus :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ### Around the Web :earth_asia: - [Montreal.AI](http://www.montreal.ai/ai4all.pdf) - [UPC-DLAI-2018](https://telecombcn-dl.github.io/2018-dlai/) - [UPC-DLAI-2019](https://telecombcn-dl.github.io/dlai-2019/) - [www.hashtagtechgeek.com](https://www.hashtagtechgeek.com/2019/10/250-machine-learning-deep-learning-videos-courseware.html) - [UPC-Barcelona, IDL-2020](https://telecombcn-dl.github.io/idl-2020/) - [UPC-DLAI-2020](https://telecombcn-dl.github.io/dlai-2020) :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ### Contributions :pray: If you find a course that fits in any of the above categories (i.e. DL, ML, RL, CV, NLP), **and** the course has lecture videos (with slides being optional), then please raise an issue or send a PR by updating the course according to the above format. :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ### Support :moneybag: **Optional:** If you're a kind Samaritan and want to support me, please do so if possible, for which I would eternally be thankful and, most importantly, your contribution imbues me with greater motivation to work, particularly in hard times :pray: [![](https://www.paypalobjects.com/en_US/i/btn/btn_donateCC_LG.gif)](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=NT3EATS5N35WU) Vielen lieben Dank! :blue_heart: :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign: ### :gift_heart: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board::mortar_board: :mortar_board: :mortar_board: :mortar_board: 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================================================ FILE: markdown2html_py/html_head.txt ================================================ Deep Learning Drizzle ================================================ FILE: markdown2html_py/index.html ================================================ Deep Learning Drizzle

Deep Learning Drizzle

"Read enough so you start developing intuitions and then trust your intuitions and go for it!"
Prof. Geoffrey Hinton, University of Toronto


Deep Learning (Deep Neural Networks)	Probabilistic Graphical Models

Machine Learning Fundamentals	Natural Language Processing

Optimization for Machine Learning	Automatic Speech Recognition

General Machine Learning	Modern Computer Vision

Reinforcement Learning	Boot Camps or Summer Schools

Bayesian Deep Learning	Medical Imaging

Graph Neural Networks	Bird's-eye view of Artificial Intelligence

Deep Learning (Deep Neural Networks)

S.No	Course Name	University/Instructor(s)	Course WebPage	Lecture Videos	Year
1.	Neural Networks for Machine Learning	Geoffrey Hinton, University of Toronto	Lecture-Slides CSC321-tijmen	YouTube-Lectures UofT-mirror	2012 2014
2.	Neural Networks Demystified	Stephen Welch, Welch Labs	Suppl. Code	YouTube-Lectures	2014
3.	Deep Learning at Oxford	Nando de Freitas, Oxford University	Oxford-ML	YouTube-Lectures	2015
4.	Deep Learning for Perception	Dhruv Batra, Virginia Tech	ECE-6504	YouTube-Lectures	2015
5.	Deep Learning	Ali Ghodsi, University of Waterloo	STAT-946	YouTube-Lectures	F2015
6.	CS231n: CNNs for Visual Recognition	Andrej Karpathy, Stanford University	CS231n	`None`	2015
7.	CS224d: Deep Learning for NLP	Richard Socher, Stanford University	CS224d	YouTube-Lectures	2015
8.	Bay Area Deep Learning	Many legends, Stanford	`None`	YouTube-Lectures	2016
9.	CS231n: CNNs for Visual Recognition	Andrej Karpathy, Stanford University	CS231n	YouTube-Lectures (Academic Torrent)	2016
10.	Neural Networks	Hugo Larochelle, Université de Sherbrooke	Neural-Networks	YouTube-Lectures (Academic Torrent)	2016

11.	CS224d: Deep Learning for NLP	Richard Socher, Stanford University	CS224d	YouTube-Lectures (Academic Torrent)	2016
12.	CS224n: NLP with Deep Learning	Richard Socher, Stanford University	CS224n	YouTube-Lectures	2017
13.	CS231n: CNNs for Visual Recognition	Justin Johnson, Stanford University	CS231n	YouTube-Lectures (Academic Torrent)	2017
14.	Topics in Deep Learning	Ruslan Salakhutdinov, CMU	10707	YouTube-Lectures	F2017
15.	Deep Learning Crash Course	Leo Isikdogan, UT Austin	`None`	YouTube-Lectures	2017
16.	Deep Learning and its Applications	François Pitié, Trinity College Dublin	EE4C16	YouTube-Lectures	2017
17.	Deep Learning	Andrew Ng, Stanford University	CS230	YouTube-Lectures	2018
18.	UvA Deep Learning	Efstratios Gavves, University of Amsterdam	UvA-DLC	Lecture-Videos	2018
19.	Advanced Deep Learning and Reinforcement Learning	Many legends, DeepMind	`None`	YouTube-Lectures	2018
20.	Machine Learning	Peter Bloem, Vrije Universiteit Amsterdam	MLVU	YouTube-Lectures	2018

21.	Deep Learning	Francois Fleuret, EPFL	EE-59	Video-Lectures	2018
22.	Introduction to Deep Learning	Alexander Amini, Harini Suresh and others, MIT	6.S191	YouTube-Lectures 2017-version	2017- 2021
23.	Deep Learning for Self-Driving Cars	Lex Fridman, MIT	6.S094	YouTube-Lectures	2017-2018
24.	Introduction to Deep Learning	Bhiksha Raj and many others, CMU	11-485/785	YouTube-Lectures	S2018
25.	Introduction to Deep Learning	Bhiksha Raj and many others, CMU	11-485/785	YouTube-Lectures Recitation-Inclusive	F2018
26.	Deep Learning Specialization	Andrew Ng, Stanford	DL.AI	YouTube-Lectures	2017-2018
27.	Deep Learning	Ali Ghodsi, University of Waterloo	STAT-946	YouTube-Lectures	F2017
28.	Deep Learning	Mitesh Khapra, IIT-Madras	CS7015	YouTube-Lectures	2018
29.	Deep Learning for AI	UPC Barcelona	DLAI-2017 DLAI-2018	YouTube-Lectures	2017-2018
30.	Deep Learning	Alex Bronstein and Avi Mendelson, Technion	CS236605	YouTube-Lectures	2018

31.	MIT Deep Learning	Many Researchers, Lex Fridman, MIT	6.S094, 6.S091, 6.S093	YouTube-Lectures	2019
32.	Deep Learning Book companion videos	Ian Goodfellow and others	DL-book slides	YouTube-Lectures	2017
33.	Theories of Deep Learning	Many Legends, Stanford	Stats-385	YouTube-Lectures (first 10 lectures)	F2017
34.	Neural Networks	Grant Sanderson	`None`	YouTube-Lectures	2017-2018
35.	CS230: Deep Learning	Andrew Ng, Kian Katanforoosh, Stanford	CS230	YouTube-Lectures	A2018
36.	Theory of Deep Learning	Lots of Legends, Canary Islands	DALI'18	YouTube-Lectures	2018
37.	Introduction to Deep Learning	Alex Smola, UC Berkeley	Stat-157	YouTube-Lectures	S2019
38.	Deep Unsupervised Learning	Pieter Abbeel, UC Berkeley	CS294-158	YouTube-Lectures	S2019
39.	Machine Learning	Peter Bloem, Vrije Universiteit Amsterdam	MLVU	YouTube-Lectures	2019
40.	Deep Learning on Computational Accelerators	Alex Bronstein and Avi Mendelson, Technion	CS236605	YouTube-Lectures	S2019

41.	Introduction to Deep Learning	Bhiksha Raj and many others, CMU	11-785	YouTube-Lectures	S2019
42.	Introduction to Deep Learning	Bhiksha Raj and many others, CMU	11-785	YouTube-Lectures Recitations	F2019
43.	UvA Deep Learning	Efstratios Gavves, University of Amsterdam	UvA-DLC	Lecture-Videos	S2019
44.	Deep Learning	Prabir Kumar Biswas, IIT Kgp	`None`	YouTube-Lectures	2019
45.	Deep Learning and its Applications	Aditya Nigam, IIT Mandi	CS-671	YouTube-Lectures	2019
46.	Neural Networks	Neil Rhodes, Harvey Mudd College	CS-152	YouTube-Lectures	F2019
47.	Deep Learning	Thomas Hofmann, ETH Zürich	DAL-DL	Lecture-Videos	F2019
48.	Deep Learning	Milan Straka, Charles University	NPFL114	Lecture-Videos	S2019
49.	UvA Deep Learning	Efstratios Gavves, University of Amsterdam	UvA-DLC-19	Lecture-Videos	F2019
50.	Artificial Intelligence: Principles and Techniques	Percy Liang and Dorsa Sadigh, Stanford University	CS221	YouTube-Lectures	F2019

51.	Analyses of Deep Learning	Lots of Legends, Stanford University	STATS-385	YouTube-Lectures	2017-2019
52.	Deep Learning Foundations and Applications	Debdoot Sheet and Sudeshna Sarkar, IIT-Kgp	AI61002	YouTube-Lectures	S2020
53.	Designing, Visualizing, and Understanding Deep Neural Networks	John Canny, UC Berkeley	CS 182/282A	YouTube-Lectures	S2020
54.	Deep Learning	Yann LeCun and Alfredo Canziani, NYU	DS-GA 1008	YouTube-Lectures	S2020
55.	Introduction to Deep Learning	Bhiksha Raj, CMU	11-785	YouTube-Lectures	S2020
56.	Deep Unsupervised Learning	Pieter Abbeel, UC Berkeley	CS294-158	YouTube-Lectures	S2020
57.	Machine Learning	Peter Bloem, Vrije Universiteit Amsterdam	VUML	YouTube-Lectures	S2020
58.	Deep Learning (with PyTorch)	Alfredo Canziani and Yann LeCun, NYU	DS-GA 1008	YouTube-Lectures	S2020
59.	Introduction to Deep Learning and Generative Models	Sebastian Raschka, UW-Madison	Stat453	YouTube-Lectures	S2020
60.	Deep Learning	Andreas Maier, FAU Erlangen-Nürnberg	DL-2020	YouTube-Lectures Lecture-Videos	SS2020

61.	Introduction to Deep Learning	Laura Leal-Taixé and Matthias Niessner, TU-München	I2DL-IN2346	YouTube-Lectures	SS2020
62.	Deep Learning	Sargur Srihari, SUNY-Buffalo	CSE676	YouTube-Lectures-P1 YouTube-Lectures-P2	2020
63.	Deep Learning Lecture Series	Lots of Legends, DeepMind x UCL, London	DLLS-20	YouTube-Lectures	2020
64.	MultiModal Machine Learning	Louis-Philippe Morency & others, Carnegie Mellon University	11-777 MMML-20	YouTube-Lectures	F2020
65.	Reliable and Interpretable Artificial Intelligence	Martin Vechev, ETH Zürich	RIAI-20	YouTube-Lectures	F2020
66.	Fundamentals of Deep Learning	David McAllester, Toyota Technological Institute, Chicago	TTIC-31230	YouTube-Lectures	F2020
67.	Foundations of Deep Learning	Soheil Feize, University of Maryland, College Park	CMSC 828W	YouTube-Lectures	F2020
68.	Deep Learning	Andreas Geiger, Universität Tübingen	DL-UT	YouTube-Lectures	W20/21
69.	Deep Learning	Andreas Maier, FAU Erlangen-Nürnberg	DL-FAU	YouTube-Lectures	W20/21
70.	Fundamentals of Deep Learning	Terence Parr and Yannet Interian, University of San Francisco	DL-Fundamentals	YouTube-Lectures	S2021

71.	Full Stack Deep Learning	Pieter Abbeel, Sergey Karayev, UC Berkeley	FS-DL	YouTube-Lectures	S2021
72.	Deep Learning: Designing, Visualizing, and Understanding DNNs	Sergey Levine, UC Berkeley	CS 182	YouTube-Lectures	S2021
73.	Deep Learning in the Life Sciences	Manolis Kellis, MIT	6.874	YouTube-Lectures	S2021
74.	Introduction to Deep Learning and Generative Models	Sebastian Raschka, University of Wisconsin-Madison	Stat 453	YouTube-Lectures	S2021
75.	Deep Learning	Alfredo Canziani and Yann LeCun, NYU	NYU-DLSP21	YouTube-Lectures	S2021
76.	Applied Deep Learning	Alexander Pacha, TU Wien	`None`	YouTube-Lectures	2020-2021
77.	Machine Learning	Hung-yi Lee, National Taiwan University	ML'21	YouTube-Lectures	S2021
78.	Mathematics of Deep Learning	Lots of legends, FAU	MoDL	Lecture-Videos	2019-21
79.	Deep Learning	Peter Bloem, Michael Cochez, and Jakub Tomczak, VU-Amsterdam	DL	YouTube-Lectures	2020-21
80.	Applied Deep Learning	Maziar Raissi, UC Boulder	ADL'21	YouTube-Lectures	2021

81.	An Introduction to Group Equivariant Deep Learning	Erik J. Bekkers, Universiteit van Amsterdam	UvAGEDL	YouTube-Lectures	2022

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Machine Learning Fundamentals

S.No	Course Name	University/Instructor(s)	Course Webpage	Video Lectures	Year
1.	Linear Algebra	Gilbert Strang, MIT	18.06 SC	YouTube-Lectures	2011
2.	Probability Primer	Jeffrey Miller, Brown University	`mathematical monk`	YouTube-Lectures	2011
3.	Information Theory, Pattern Recognition, and Neural Networks	David Mackay, University of Cambridge	ITPRNN	YouTube-Lectures	2012
4.	Linear Algebra Review	Zico Kolter, CMU	LinAlg	YouTube-Lectures	2013
5.	Probability and Statistics	Michel van Biezen	`None`	YouTube-Lectures	2015
6.	Linear Algebra: An in-depth Introduction	Pavel Grinfeld	`None`	Part-1 Part-2 Part-3 Part-4	2015- 2017
7.	Multivariable Calculus	Grant Sanderson, Khan Academy	`None`	YouTube-Lectures	2016
8.	Essence of Linear Algebra	Grant Sanderson	`None`	YouTube-Lectures	2016
9.	Essence of Calculus	Grant Sanderson	`None`	YouTube-Lectures	2017-2018
10.	Math Background for Machine Learning	Geoff Gordon, CMU	10-606, 10-607	YouTube-Lectures	F2017

11.	Mathematics for Machine Learning (Linear Algebra, Calculus)	David Dye, Samuel Cooper, and Freddie Page, IC-London	MML	YouTube-Lectures	2018
12.	Multivariable Calculus	S.K. Gupta and Sanjeev Kumar, IIT-Roorkee	MVC	YouTube-Lectures	2018
13.	Engineering Probability	Rich Radke, Rensselaer Polytechnic Institute	`None`	YouTube-Lectures	2018
14.	Matrix Methods in Data Analysis, Signal Processing, and Machine Learning	Gilbert Strang, MIT	18.065	YouTube-Lectures	S2018
15.	Information Theory	Himanshu Tyagi, IISC, Bengaluru	E2 201	YouTube-Lectures	2018-20
16.	Math Camp	Mark Walker, University of Arizona	UAMathCamp / Econ-519	YouTube-Lectures	2019
17.	A 2020 Vision of Linear Algebra	Gilbert Strang, MIT	VoLA	YouTube-Lectures	S2020
18.	Mathematics for Numerical Computing and Machine Learning	Szymon Rusinkiewicz, Princeton University	COS-302	YouTube-Lectures	F2020
19.	Essential Statistics for Neuroscientists	Philipp Berens, Universität Klinikum Tübingen	`None`	YouTube-Lectures	2020
20.	Mathematics for Machine Learning	Ulrike von Luxburg, Eberhard Karls Universität Tübingen	Math4ML	YouTube-Lectures	W2020
21.	Introduction to Causal Inference	Brady Neal, Mila, Montréal	CausalInf	YouTube-Lectures	F2020
22.	Applied Linear Algebra	Andrew Thangaraj, IIT Madras	EE5120	YouTube-Lectures	2021
23.	Mathematical Tools for Data Science	Carlos Fernandez-Granda, New York University	DS-GA 1013/Math-GA 2824	YouTube-Lectures	2021
24.	Mathematics for Numerical Computing and Machine Learning	Ryan Adams, Princeton University	COS 302 / SML 305	YouTube-Lectures	2021

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Optimization for Machine Learning

S.No	Course Name	University/Instructor(s)	Course Webpage	Video Lectures	Year
1.	Convex Optimization	Stephen Boyd, Stanford University	ee364a	YouTube-Lectures	2008
2.	Introduction to Optimization	Michael Zibulevsky, Technion	CS-236330	YouTube-Lectures	2009
3.	Optimization for Machine Learning	S V N Vishwanathan, Purdue University	`None`	YouTube-Lectures	2011
4.	Optimization	Geoff Gordon & Ryan Tibshirani, CMU	10-725	YouTube-Lectures	2012
5.	Convex Optimization	Joydeep Dutta, IIT-Kanpur	cvx-nptel	YouTube-Lectures	2013
6.	Foundations of Optimization	Joydeep Dutta, IIT-Kanpur	fop-nptel	YouTube-Lectures	2014
7.	Algorithmic Aspects of Machine Learning	Ankur Moitra, MIT	18.409-AAML	YouTube-Lectures	S2015
8.	Numerical Optimization	Shirish K. Shevade, IISC	`None`	YouTube-Lectures	2015
9.	Convex Optimization	Ryan Tibshirani, CMU	10-725	YouTube-Lectures	S2015
10.	Convex Optimization	Ryan Tibshirani, CMU	10-725	YouTube-Lectures	F2015
11.	Advanced Algorithms	Ankur Moitra, MIT	6.854-AA	YouTube-Lectures	S2016
12.	Introduction to Optimization	Michael Zibulevsky, Technion	`None`	YouTube-Lectures	2016
13.	Convex Optimization	Javier Peña & Ryan Tibshirani	10-725/36-725	YouTube-Lectures	F2016
14.	Convex Optimization	Ryan Tibshirani, CMU	10-725	YouTube-Lectures Lecture-Videos	F2018
15.	Modern Algorithmic Optimization	Yurii Nesterov, UCLouvain	`None`	YouTube-Lectures	2018
16.	Optimization, Foundations of Optimization	Mark Walker, University of Arizona	MathCamp-20	YouTube-Lectures-Found. YouTube-Lectures-Opt	2019 - now
17.	Optimization: Principles and Algorithms	Michel Bierlaire, École polytechnique fédérale de Lausanne (EPFL)	opt-algo	YouTube-Lectures	2019
18.	Optimization and Simulation	Michel Bierlaire, École polytechnique fédérale de Lausanne (EPFL)	opt-sim	YouTube-Lectures	S2019
19.	Brazilian Workshop on Continuous Optimization	Lots of Legends, Instituto Nacional de Matemática Pura e Aplicada, Rio de Janeiro	cont. opt.	YouTube-Lectures	2019
20.	One World Optimization Seminar	Lots of Legends, Universität Wien	1W-OPT	YouTube-Lectures	2020-

21.	Convex Optimization II	Constantine Caramanis, UT Austin	CVX-Optim-II	YouTube-Lectures	S2020
22.	Combinatorial Optimization	Constantine Caramanis, UT Austin	comb-op	YouTube-Lectures	F2020
23.	Optimization Methods for Machine Learning and Engineering	Julius Pfrommer, Jürgen Beyerer, Karlsruher Institut für Technologie (KIT)	Optim-MLE, slides	YouTube-Lectures	W2020-21

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General Machine Learning

S.No	Course Name	University/Instructor(s)	Course Webpage	Video Lectures	Year
1.	CS229: Machine Learning	Andrew Ng, Stanford University	CS229-old CS229-new	YouTube-Lectures	2007
2.	Machine Learning	Jeffrey Miller, Brown University	`mathematical monk`	YouTube-Lectures	2011
3.	Machine Learning	Tom Mitchell, CMU	10-701	Lecture-Videos	2011
4.	Machine Learning and Data Mining	Nando de Freitas, University of British Columbia	CPSC-340	YouTube-Lectures	2012
5.	Learning from Data	Yaser Abu-Mostafa, CalTech	CS156	YouTube-Lectures	2012
6.	Machine Learning	Rudolph Triebel, Technische Universität München	Machine Learning	YouTube-Lectures	2013
7.	Introduction to Machine Learning	Alex Smola, CMU	10-701	YouTube-Lectures	2013
8.	Introduction to Machine Learning	Alex Smola and Geoffrey Gordon, CMU	10-701x	YouTube-Lectures	2013
9.	Pattern Recognition	Sukhendu Das, IIT-M and C.A. Murthy, ISI-Calcutta	PR-NPTEL	YouTube-Lectures	2014
10.	An Introduction to Statistical Learning with Applications in R	Trevor Hastie and Robert Tibshirani, Stanford	stat-learn R-bloggers	YouTube-Lectures	2014

11.	Introduction to Machine Learning	Katie Malone, Sebastian Thrun, Udacity	ML-Udacity	YouTube-Lectures	2015
12.	Introduction to Machine Learning	Dhruv Batra, Virginia Tech	ECE-5984	YouTube-Lectures	2015
13.	Statistical Learning - Classification	Ali Ghodsi, University of Waterloo	STAT-441	YouTube-Lectures	2015
14.	Machine Learning Theory	Shai Ben-David, University of Waterloo	`None`	YouTube-Lectures	2015
15.	Introduction to Machine Learning	Alex Smola, CMU	10-701	YouTube-Lectures	S2015
16.	Statistical Machine Learning	Larry Wasserman, CMU	`None`	YouTube-Lectures	S2015
17.	ML: Supervised Learning	Michael Littman, Charles Isbell, Pushkar Kolhe, GaTech	ML-Udacity	YouTube-Lectures	2015
18.	ML: Unsupervised Learning	Michael Littman, Charles Isbell, Pushkar Kolhe, GaTech	ML-Udacity	YouTube-Lectures	2015
19.	Advanced Introduction to Machine Learning	Barnabas Poczos and Alex Smola	10-715	YouTube-Lectures	F2015
20.	Machine Learning	Pedro Domingos, UWashington	CSEP-546	YouTube-Lectures	S2016

21.	Statistical Machine Learning	Larry Wasserman, CMU	`None`	YouTube-Lectures	S2016
22.	Machine Learning with Large Datasets	William Cohen, CMU	10-605	YouTube-Lectures	F2016
23.	Math Background for Machine Learning	Geoffrey Gordon, CMU	`10-600`	YouTube-Lectures	F2016
24.	Statistical Learning - Classification	Ali Ghodsi, University of Waterloo	`None`	YouTube-Lectures	2017
25.	Machine Learning	Andrew Ng, Stanford University	Coursera-ML	YouTube-Lectures	2017
26.	Machine Learning	Roni Rosenfield, CMU	10-601	YouTube-Lectures	2017
27.	Statistical Machine Learning	Ryan Tibshirani, Larry Wasserman, CMU	10-702	YouTube-Lectures	S2017
28.	Machine Learning for Computer Vision	Fred Hamprecht, Heidelberg University	`None`	YouTube-Lectures	F2017
29.	Math Background for Machine Learning	Geoffrey Gordon, CMU	10-606 / 10-607	YouTube-Lectures	F2017
30.	Data Visualization	Ali Ghodsi, University of Waterloo	`None`	YouTube-Lectures	2017

31.	Machine Learning for Physicists	Florian Marquardt, Uni Erlangen-Nürnberg	ML4Phy-17	Lecture-Videos	2017
32.	Machine Learning for Intelligent Systems	Kilian Weinberger, Cornell University	CS4780	YouTube-Lectures	F2018
33.	Statistical Learning Theory and Applications	Tomaso Poggio, Lorenzo Rosasco, Sasha Rakhlin	9.520/6.860	YouTube-Lectures	F2018
34.	Machine Learning and Data Mining	Mike Gelbart, University of British Columbia	CPSC-340	YouTube-Lectures	2018
35.	Foundations of Machine Learning	David Rosenberg, Bloomberg	FOML	YouTube-Lectures	2018
36.	Introduction to Machine Learning	Andreas Krause, ETH Zürich	IntroML	YouTube-Lectures	2018
37.	Machine Learning Fundamentals	Sanjoy Dasgupta, UC-San Diego	MLF-slides	YouTube-Lectures	2018
38.	Machine Learning	Jordan Boyd-Graber, University of Maryland	CMSC-726	YouTube-Lectures	2015-2018
39.	Machine Learning	Andrew Ng, Stanford University	CS229	YouTube-Lectures	2018
40.	Machine Intelligence	H.R.Tizhoosh, UWaterloo	SYDE-522	YouTube-Lectures	2019

41.	Introduction to Machine Learning	Pascal Poupart, University of Waterloo	CS480/680	YouTube-Lectures	S2019
42.	Advanced Machine Learning	Thorsten Joachims, Cornell University	CS-6780	Lecture-Videos	S2019
43.	Machine Learning for Structured Data	Matt Gormley, Carnegie Mellon University	10-418/10-618	YouTube-Lectures	F2019
44.	Advanced Machine Learning	Joachim Buhmann, ETH Zürich	ML2-AML	Lecture-Videos	F2019
45.	Machine Learning for Signal Processing	Vipul Arora, IIT-Kanpur	MLSP	Lecture-Videos	F2019
46.	Foundations of Machine Learning	Animashree Anandkumar, CalTech	CMS-165	YouTube-Lectures	2019
47.	Machine Learning for Physicists	Florian Marquardt, Uni Erlangen-Nürnberg	`None`	Lecture-Videos	2019
48.	Applied Machine Learning	Andreas Müller, Columbia University	COMS-W4995	YouTube-Lectures	2019
49.	Fundamentals of Machine Learning over Networks	Hossein Shokri-Ghadikolaei, KTH, Sweden	MLoNs	YouTube-Lectures	2019
50.	Foundations of Machine Learning and Statistical Inference	Animashree Anandkumar, CalTech	CMS-165	YouTube-Lectures	2020

51.	Machine Learning	Rebecca Willett and Yuxin Chen, University of Chicago	STAT 37710 / CMSC 35400	Lecture-Videos	S2020
52.	Introduction to Machine Learning	Sanjay Lall and Stephen Boyd, Stanford University	EE104/CME107	YouTube-Lectures	S2020
53.	Applied Machine Learning	Andreas Müller, Columbia University	COMS-W4995	YouTube-Lectures	S2020
54.	Statistical Machine Learning	Ulrike von Luxburg, Eberhard Karls Universität Tübingen	Stat-ML	YouTube-Lectures	SS2020
55.	Probabilistic Machine Learning	Philipp Hennig, Eberhard Karls Universität Tübingen	Prob-ML	YouTube-Lectures	SS2020
56.	Machine Learning	Sarath Chandar, PolyMTL, UdeM, Mila	INF8953CE	YouTube-Lectures	F2020
57.	Machine Learning	Erik Bekkers, Universiteit van Amsterdam	UvA-ML	YouTube-Lectures	F2020
58.	Neural Networks for Signal Processing	Shayan Srinivasa Garani, Indian Institute of Science	NN4SP	YouTube-Lectures	F2020
59.	Introduction to Machine Learning	Dmitry Kobak, Universität Klinikum Tübingen	`None`	YouTube-Lectures	2020
60.	Machine Learning (PRML)	Erik J. Bekkers, Universiteit van Amsterdam	UvAML-1	YouTube-Lectures	2020

61.	Machine Learning with Kernel Methods	Julien Mairal and Jean-Philippe Vert, Inria/ENS Paris-Saclay, Google	ML-Kernels	YouTube-Lectures	S2021
62.	Continual Learning	Vincenzo Lomonaco, Università di Pisa	ContLearn'21	YouTube-Lectures	2021
63.	Causality	Christina Heinze-Deml, ETH Zurich	Causal'21	YouTube-Lectures	2021

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Reinforcement Learning

S.No	Course Name	University/Instructor(s)	Course Webpage	Video Lectures	Year
1.	A Short Course on Reinforcement Learning	Satinder Singh, UMichigan	`None`	YouTube-Lectures	2011
2.	Approximate Dynamic Programming	Dimitri P. Bertsekas, MIT	Lecture-Slides	YouTube-Lectures	2014
3.	Introduction to Reinforcement Learning	David Silver, DeepMind	UCL-RL	YouTube-Lectures	2015
4.	Reinforcement Learning	Charles Isbell, Chris Pryby, GaTech; Michael Littman, Brown	RL-Udacity	YouTube-Lectures	2015
5.	Reinforcement Learning	Balaraman Ravindran, IIT Madras	RL-IITM	YouTube-Lectures	2016
6.	Deep Reinforcement Learning	Sergey Levine, UC Berkeley	CS-294	YouTube-Lectures	S2017
7.	Deep Reinforcement Learning	Sergey Levine, UC Berkeley	CS-294	YouTube-Lectures	F2017
8.	Deep RL Bootcamp	Many legends, UC Berkeley	Deep-RL	YouTube-Lectures	2017
9	Data Efficient Reinforcement Learning	Lots of Legends, Canary Islands	DERL-17	YouTube-Lectures	2017
10.	Deep Reinforcement Learning	Sergey Levine, UC Berkeley	CS-294-112	YouTube-Lectures	2018

11.	Reinforcement Learning	Pascal Poupart, University of Waterloo	CS-885	YouTube-Lectures	2018
12.	Deep Reinforcement Learning and Control	Katerina Fragkiadaki and Tom Mitchell, CMU	10-703	YouTube-Lectures	2018
13.	Reinforcement Learning and Optimal Control	Dimitri Bertsekas, Arizona State University	RLOC	Lecture-Videos	2019
14.	Reinforcement Learning	Emma Brunskill, Stanford University	CS 234	YouTube-Lectures	2019
15.	Reinforcement Learning Day	Lots of Legends, Microsoft Research, New York	RLD-19	YouTube-Lectures	2019
16.	New Directions in Reinforcement Learning and Control	Lots of Legends, IAS, Princeton University	NDRLC-19	YouTube-Lectures	2019
17.	Deep Reinforcement Learning	Sergey Levine, UC Berkeley	CS 285	YouTube-Lectures	F2019
18.	Deep Multi-Task and Meta Learning	Chelsea Finn, Stanford University	CS 330	YouTube-Lectures	F2019
19.	RL-Theory Seminars	Lots of Legends, Earth	RL-theory-sem	YouTube-Lectures	2020 -
20.	Deep Reinforcement Learning	Sergey Levine, UC Berkeley	CS 285	YouTube-Lectures	F2020

21.	Introduction to Reinforcement Learning	Amir-massoud Farahmand, Vector Institute, University of Toronto	RL-intro	YouTube-Lectures	S2021
22.	Reinforcement Learning	Antonio Celani and Emanuele Panizon, International Centre for Theoretical Physics	`None`	YouTube-Lectures	2021
23.	Computational Sensorimotor Learning	Pulkit Agrawal, MIT-CSAIL	6.884-CSL	YouTube-Lectures	S2021
24.	Reinforcement Learning	Dimitri P. Bertsekas, ASU/MIT	RL-21	YouTube-Lectures	S2021
25.	Reinforcement Learning	Sarath Chandar, École Polytechnique de Montréal	INF8953DE	YouTube-Lectures	F2021
26.	Deep Reinforcement Learning	Sergey Levine, UC Berkeley	CS 285	YouTube-Lectures	F2021
27.	Reinforcement Learning Lecture Series	Lots of Legends, DeepMind & UC London	RL-series	YouTube-Lectures	2021
28.	Reinforcement Learning	Dimitri P. Bertsekas, ASU/MIT	RL-22	YouTube-Lectures	S2022

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Probabilistic Graphical Models

S.No	Course Name	University/Instructor(s)	Course WebPage	Lecture Videos	Year
1.	Probabilistic Graphical Models	Many Legends, MPI-IS	MLSS-Tuebingen	YouTube-Lectures	2013
2.	Probabilistic Modeling and Machine Learning	Zoubin Ghahramani, University of Cambridge	WUST-Wroclaw	YouTube-Lectures	2013
3.	Probabilistic Graphical Models	Eric Xing, CMU	10-708	YouTube-Lectures	2014
4.	Learning with Structured Data: An Introduction to Probabilistic Graphical Models	Christoph Lampert, IST Austria	`None`	YouTube-Lectures	2016
5.	Probabilistic Graphical Models	Nicholas Zabaras, University of Notre Dame	PGM	YouTube-Lectures	2018
6.	Probabilistic Graphical Models	Eric Xing, CMU	10-708	Lecture-Videos YouTube-Lectures	S2019
7.	Probabilistic Graphical Models	Eric Xing, CMU	10-708	YouTube-Lectures	S2020
8.	Uncertainty Modeling in AI	Gim Hee Lee, National University of Singapura (NUS)	CS 5340 - CH, CS 5340-NB	YouTube-Lectures	2020-21

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Bayesian Deep Learning

S.No	Course Name	University/Instructor(s)	Course WebPage	Lecture Videos	Year
1.	Bayesian Neural Networks, Variational Inference	Lots of Legends	`None`	YouTube-Lectures	2014-now
2.	Variational Inference	Chieh Wu, Northeastern University	`None`	YouTube-Lectures	2015
3.	Deep Learning and Bayesian Methods	Lots of Legends, HSE Moscow	DLBM-SS	YouTube-Lectures	2018
4.	Deep Learning and Bayesian Methods	Lots of Legends, HSE Moscow	DLBM-SS	YouTube-Lectures	2019
5.	Nordic Probabilistic AI	Lots of Legends, NTNU, Trondheim	ProbAI	YouTube-Lectures	2019

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Medical Imaging

S.No	Course Name	University/Instructor(s)	Course WebPage	Lecture Videos	Year
1.	Medical Imaging Summer School	Lots of Legends, Sicily	MISS-14	YouTube-Lectures	2014
2.	Biomedical Image Analysis Summer School	Lots of Legends, Paris	`None`	YouTube-Lectures	2015
3.	Medical Imaging Summer School	Lots of Legends, Sicily	MISS-16	YouTube-Lectures	2016
4.	OPtical and UltraSound imaging - OPUS	Lots of Legends, Université de Lyon, France	OPUS'16	YouTube-Lectures	2016
5.	Medical Imaging Summer School	Lots of Legends, Sicily	MISS-18	YouTube-Lectures	2018
6.	Seminar on AI in Healthcare	Lots of Legends, Stanford	CS 522	YouTube-Lectures	2018
7.	Machine Learning for Healthcare	David Sontag, Peter Szolovits, CSAIL MIT	MLHC-19 MIT 6.S897	YouTube-Lectures	S2019
8.	Deep Learning and Medical Applications	Lots of Legends, IPAM, UCLA	DLM-20	Lecture-Videos	2020
9.	Stanford Symposium on Artificial Intelligence in Medicine and Imaging	Lots of Legends, Stanford AIMI	AIMI-20	YouTube-Lectures	2020

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Graph Neural Networks (Geometric DL)

S.No	Course Name	University/Instructor(s)	Course WebPage	Lecture Videos	Year
1.	Deep learning on graphs and manifolds	Michael Bronstein, Technion	`None`	YouTube-Lectures	2017
2.	Geometric Deep Learning on Graphs and Manifolds	Michael Bronstein, Technische Universität München	`None`	Lec-part1, Lec-part2	2017
3.	Eurographics Symposium on Geometry Processing - Graduate School	Lots of Legends, SIGGRAPH, London	SGP-2017	YouTube-Lectures	2017
4.	Eurographics Symposium on Geometry Processing - Graduate School	Lots of Legends, SIGGRAPH, Paris	SGP-2018	YouTube-Lectures	2018
5.	Analysis of Networks: Mining and Learning with Graphs	Jure Leskovec, Stanford University	CS224W	Lecture-Videos	2018
6.	Machine Learning with Graphs	Jure Leskovec, Stanford University	CS224W	YouTube-Lectures	2019
7.	Geometry and Learning from Data in 3D and Beyond -Geometry and Learning from Data Tutorials	Lots of Legends, IPAM UCLA	GLDT	Lecture-Videos	2019
8.	Geometry and Learning from Data in 3D and Beyond - Geometric Processing	Lots of Legends, IPAM UCLA	GeoPro	Lecture-Videos	2019
9.	Geometry and Learning from Data in 3D and Beyond - Shape Analysis	Lots of Legends, IPAM UCLA	Shape-Analysis	Lecture-Videos	2019
10.	Geometry and Learning from Data in 3D and Beyond - Geometry of Big Data	Lots of Legends, IPAM UCLA	Geo-BData	Lecture-Videos	2019

11.	Geometry and Learning from Data in 3D and Beyond - Deep Geometric Learning of Big Data and Applications	Lots of Legends, IPAM UCLA	DGL-BData	Lecture-Videos	2019
12.	Israeli Geometric Deep Learning	Lots of Legends, Israel	iGDL-20	Lecture-Videos	2020
13.	Machine Learning for Graphs and Sequential Data	Stephan Günnemann, Technische Universität München (TUM)	MLGS-20	Lecture-Videos	S2020
14.	Machine Learning with Graphs	Jure Leskovec, Stanford	CS224W	YouTube-Lectures	W2021
15.	Geometric Deep Learning - AMMI	Lots of Legends, Virtual	GDL-AMMI	YouTube-Lectures	2021
16.	Summer School on Geometric Deep Learning -	Lots of Legends, DTU, DIKU & AAU	GDL- DTU, DIKU & AAU	Lecture-Videos	2021
17.	Graph Neural Networks	Alejandro Ribeiro, University of Pennsylvania	ESE 514	YouTube-Lectures	F2021

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Natural Language Processing

S.No	Course Name	University/Instructor(s)	Course WebPage	Lecture Videos	Year
1.	Computational Linguistics I	Jordan Boyd-Graber, University of Maryland	CMS-723	YouTube-Lectures	2013-2018
2.	Deep Learning for Natural Language Processing	Nils Reimers, TU Darmstadt	DL4NLP	YouTube-Lectures	2015-2017
3.	Deep Learning for Natural Language Processing	Many Legends, DeepMind-Oxford	DL-NLP	YouTube-Lectures	2017
4.	Deep Learning for Speech & Language	UPC Barcelona	DL-SL	Lecture-Videos	2017
5.	Neural Networks for Natural Language Processing	Graham Neubig, CMU	NN4NLP Code	YouTube-Lectures	2017
6.	Neural Networks for Natural Language Processing	Graham Neubig, CMU	NN4-NLP	YouTube-Lectures	2018
7.	Deep Learning for NLP	Min-Yen Kan, NUS	CS-6101	YouTube-Lectures	2018
8.	Neural Networks for Natural Language Processing	Graham Neubig, CMU	NN4NLP	YouTube-Lectures	2019
9.	Natural Language Processing with Deep Learning	Abigail See, Chris Manning, Richard Socher, Stanford University	CS224n	YouTube-Lectures	2019
10.	Natural Language Understanding	Bill MacCartney and Christopher Potts	CS224U	YouTube-Lectures	S2019

11.	Neural Networks for Natural Language Processing	Graham Neubig, Carnegie Mellon University	CS 11-747	YouTube-Lectures	S2020
12.	Advanced Natural Language Processing	Mohit Iyyer, UMass Amherst	CS 685	YouTube-Lectures	F2020
13.	Machine Translation	Philipp Koehn, Johns Hopkins University	EN 601.468/668	YouTube-Lectures	F2020
14.	Neural Networks for NLP	Graham Neubig, Carnegie Mellon University	CS 11-747	YouTube-Lectures	2021
15.	Deep Learning for Natural Language Processing	Kyunghyun Cho, New York University	DS-GA 1011	YouTube-Lectures	F2021
16.	Natural Language Processing with Deep Learning	Chris Manning, Stanford University	CS224n	YouTube-Lectures	2021

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Automatic Speech Recognition

S.No	Course Name	University/Instructor(s)	Course WebPage	Lecture Videos	Year
1.	Deep Learning for Speech & Language	UPC Barcelona	DL-SL	Lecture-Videos YouTube-Videos	2017
2.	Speech and Audio in the Northeast	Many Legends, Google NYC	SANE-15	YouTube-Videos	2015
3.	Automatic Speech Recognition	Samudra Vijaya K, TIFR	`None`	YouTube-Videos	2016
4.	Speech and Audio in the Northeast	Many Legends, Google NYC	SANE-17	YouTube-Videos	2017
5.	Speech and Audio in the Northeast	Many Legends, Google Cambridge	SANE-18	YouTube-Videos	2018

-1.	Deep Learning for Speech Recognition	Many Legends, AoE	`None`	YouTube-Videos	2015-2018

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Modern Computer Vision

S.No	Course Name	University/Instructor(s)	Course WebPage	Lecture Videos	Year
1.	Microsoft Computer Vision Summer School - (classical)	Lots of Legends, Lomonosov Moscow State University	`None`	YouTube-Videos Russian-mirror	2011
2.	Computer Vision - (classical)	Mubarak Shah, UCF	CAP-5415	YouTube-Lectures	2012
3.	Image and Multidimensional Signal Processing - (classical)	William Hoff, Colorado School of Mines	CSCI 510/EENG 510	YouTube-Lectures	2012
4.	Computer Vision - (classical)	William Hoff, Colorado School of Mines	CSCI 512/EENG 512	YouTube-Lectures	2012
5.	Image and Video Processing: From Mars to Hollywood with a Stop at the Hospital	Guillermo Sapiro, Duke University	`None`	YouTube-Videos	2013
6.	Multiple View Geometry (classical)	Daniel Cremers, Technische Universität München	mvg	YouTube-Lectures	2013
7.	Mathematical Methods for Robotics, Vision, and Graphics	Justin Solomon, Stanford University	CS-205A	YouTube-Lectures	2013
8.	Computer Vision - (classical)	Mubarak Shah, UCF	CAP-5415	YouTube-Lectures	2014
9.	Computer Vision for Visual Effects (classical)	Rich Radke, Rensselaer Polytechnic Institute	ECSE-6969	YouTube-Lectures	S2014
10.	Autonomous Navigation for Flying Robots	Juergen Sturm, Technische Universität München	Autonavx	YouTube-Lectures	2014

11.	SLAM - Mobile Robotics	Cyrill Stachniss, Universitaet Freiburg	RobotMapping	YouTube-Lectures	2014
12.	Computational Photography	Irfan Essa, David Joyner, Arpan Chakraborty	CP-Udacity	YouTube-Lectures	2015
13.	Introduction to Digital Image Processing	Rich Radke, Rensselaer Polytechnic Institute	ECSE-4540	YouTube-Lectures	S2015
14.	Lectures on Digital Photography	Marc Levoy, Stanford/Google Research	LoDP	YouTube-Lectures	2016
15.	Introduction to Computer Vision (foundation)	Aaron Bobick, Irfan Essa, Arpan Chakraborty	CV-Udacity	YouTube-Lectures	2016
16.	Computer Vision	Syed Afaq Ali Shah, University of Western Australia	`None`	YouTube-Lectures	2016
17.	Photogrammetry I & II	Cyrill Stachniss, University of Bonn	PG-I&II	YouTube-Lectures	2016
18.	Deep Learning for Computer Vision	UPC Barcelona	DLCV-16 DLCV-17 DLCV-18	YouTube-Lectures	2016-2018
19.	Convolutional Neural Networks	Andrew Ng, Stanford University	DeepLearning.AI	YouTube-Lectures	2017
20.	Variational Methods for Computer Vision	Daniel Cremers, Technische Universität München	VMCV	YouTube-Lectures	2017

21.	Winter School on Computer Vision	Lots of Legends, Israel Institute for Advanced Studies	WS-CV	YouTube-Lectures	2017
22.	Deep Learning for Visual Computing	Debdoot Sheet, IIT-Kgp	Nptel Notebooks	YouTube-Lectures	2018
23.	The Ancient Secrets of Computer Vision	Joseph Redmon, Ali Farhadi	TASCV ; TASCV-UW	YouTube-Lectures	2018
24.	Modern Robotics	Kevin Lynch, Northwestern Robotics	modern-robot	YouTube-Lectures	2018
25.	Digial Image Processing	Alex Bronstein, Technion	CS236860	YouTube-Lectures	2018
26.	Mathematics of Imaging - Variational Methods and Optimization in Imaging	Lots of Legends, Institut Henri Poincaré	Workshop-1	YouTube-Lectures	2019
27.	Deep Learning for Video	Xavier Giró, UPC Barcelona	deepvideo	YouTube-Lectures	2019
28.	Statistical modeling for shapes and imaging	Lots of Legends, Institut Henri Poincaré, Paris	workshop-2	YouTube-Lectures	2019
29.	Imaging and machine learning	Lots of Legends, Institut Henri Poincaré, Paris	workshop-3	YouTube-Lectures	2019
30.	Computer Vision	Jayanta Mukhopadhyay, IIT Kgp	CV-nptel	YouTube-Lectures	2019

31.	Deep Learning for Computer Vision	Justin Johnson, UMichigan	EECS 498-007	Lecture-Videos YouTube-Lectures	2019
32.	Sensors and State Estimation 2	Cyrill Stachniss, University of Bonn	`None`	YouTube-Lectures	S2020
33.	Computer Vision III: Detection, Segmentation and Tracking	Laura Leal-Taixé, TU München	CV3DST	YouTube-Lectures	S2020
34.	Advanced Deep Learning for Computer Vision	Laura Leal-Taixé and Matthias Nießner, TU München	ADL4CV	YouTube-Lectures	S2020
35.	Computer Vision: Foundations	Fred Hamprecht, Universität Heidelberg	CVF	YouTube-Lectures	SS2020
36.	MIT Vision Seminar	Lots of Legends, MIT	MIT-Vision	YouTube-Lectures	2015-now
37.	TUM AI Guest Lectures	Lots of Legends, Technische Universität München	TUM-AI	YouTube-Lectures	2020 - now
38.	Seminar on 3D Geometry & Vision	Lots of Legends, Virtual	3DGV seminar	YouTube-Lectures	2020 - now
39.	Event-based Robot Vision	Guillermo Gallego, Technische Universität Berlin	EVIS-SS20	YouTube-Lectures	2020 - now
40.	Deep Learning for Computer Vision	Vineeth Balasubramanian, IIT Hyderabad	DL-CV'20	YouTube-Lectures	2020

41.	Deep Learning for Visual Computing	Peter Wonka, KAUST, SA	`NOne`	YouTube-Lectures	2020
42.	Computer Vision	Yogesh Rawat, University of Central Florida	CAP5415-CV	YouTube-Lectures	F2020
43.	Multimedia Signal Processing	Mark Hasegawa-Johnson, UIUC	ECE-417 MSP	Lecture Videos	F2020
44.	Computer Vision	Andreas Geiger, Universität Tübingen	Comp.Vis	YouTube-Lectures	S2021
45.	3D Computer Vision	Lee Gim Hee, National Univeristy of Singapura	`None`	YouTube-Lectures	2021
46.	Deep Learning for Computer Vision: Fundamentals and Applications	T. Dekel et al., Weizmann Institute of Science	DL4CV	YouTube-Lectures	S2021
47.	Current Topics in ML Methods in 3D and Geometric Deep Learning	Animesh Garg & others, University of Toronto	CSC 2547	YouTube-Lectures	2021
48.	First Principles of Computer Vision	Shree K. Nayar, Columbia University	FPCV	YouTube-Lectures	2021
49.	Self-Driving Cars	Andreas Geiger, Universität Tübingen	SDC'21	YouTube-Lectures	W2021

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Boot Camps or Summer Schools

S.No	Course Name	University/Instructor(s)	Course WebPage	Lecture Videos	Year
1.	Deep Learning, Feature Learning	Lots of Legends, IPAM UCLA	GSS-2012	YouTube-Lectures	2012
2.	Big Data Boot Camp	Lots of Legends, Simons Institute	Big Data	YouTube-Lectures	2013
3.	Machine Learning Summer School	Lots of Legends, MPI-IS Tübingen	MLSS-13	YouTube-Lectures	2013
4	Graduate Summer School: Computer Vision	Lots of Legends, IPAM-UCLA	GSS-CV	Video-Lectures	2013
5.	Machine Learning Summer School	Lots of Legends, Reykjavik University	MLSS-14	YouTube-Lectures	2014
6.	Machine Learning Summer School	Lots of Legends, Pittsburgh	MLSS-14	YouTube-Lectures	2014
7.	Deep Learning Summer School	Lots of Legends, Université de Montréal	DLSS-15	YouTube-Lectures	2015
8.	Biomedical Image Analysis Summer School	Lots of Legends, CentraleSupelec, Paris	`None`	YouTube-Lectures	2015
9.	Mathematics of Signal Processing	Lots of Legends, Hausdorff Institute for Mathematics	SigProc	YouTube-Lectures	2016
10.	Microsoft Research - Machine Learning Course	S V N Vishwanathan and Prateek Jain MS-Research	`None`	YouTube-Lectures	2016

11.	Deep Learning Summer School	Lots of Legends, Université de Montréal	DL-SS-16	YouTube-Lectures	2016
12.	Lisbon Machine Learning School	Lots of Legends, Instituto Superior Técnico, Portugal	LxMLS-16	YouTube-Lectures	2016
13.	Machine Learning Advances and Applications Seminar	Lots of Legends, Fields Institute, University of Toronto	MLAAS-16	YouTube-Lectures Video-Lectures	2016-2017
14.	Machine Learning Advances and Applications Seminar	Lots of Legends, Fields Institute, University of Toronto	MLAAS-17	Video Lectures	2017-2018
15.	Machine Learning Summer School	Lots of Legends, MPI-IS Tübingen	MLSS-17	YouTube-Lectures	2017
16.	Representation Learning	Lots of Legends, Simons Institute	RepLearn	YouTube-Lectures	2017
17.	Foundations of Machine Learning	Lots of Legends, Simons Institute	ML-BootCamp	YouTube-Lectures	2017
18.	Optimization, Statistics, and Uncertainty	Lots of Legends, Simons Institute	Optim-Stats	YouTube-Lectures	2017
19.	Deep Learning: Theory, Algorithms, and Applications	Lots of Legends, TU-Berlin	DL: TAA	YouTube-Lectures	2017
20.	Deep Learning and Reinforcement Learning Summer School	Lots of Legends, Université de Montréal	DLRL-2017	Lecture-videos	2017

21.	Statistical Physics Methods in Machine Learning	Lots of Legends, International Centre for Theoretical Sciences, TIFR	SPMML	YouTube-Lectures	2017
22.	Lisbon Machine Learning School	Lots of Legends, Instituto Superior Técnico, Portugal	LxMLS-17	YouTube-Lectures	2017
23.	Interactive Learning	Lots of Legends, Simons Institute, Berkeley	IL-2017	YouTube-Lectures	2017
24.	Computational Challenges in Machine Learning	Lots of Legends, Simons Institute, Berkeley	CCML-17	YouTube-Lectures	2017
25.	Foundations of Data Science	Lots of Legends, Simons Institute	DS-BootCamp	YouTube-Lectures	2018
26.	Deep Learning and Bayesian Methods	Lots of Legends, HSE Moscow	DLBM-SS	YouTube-Lectures	2018
27.	New Deep Learning Techniques	Lots of Legends, IPAM UCLA	IPAM-Workshop	YouTube-Lectures	2018
28.	Deep Learning and Reinforcement Learning Summer School	Lots of Legends, University of Toronto	DLRL-2018	Lecture-videos	2018
29.	Machine Learning Summer School	Lots of Legends, Universidad Autónoma de Madrid, Spain	MLSS-18	YouTube-Lectures Course-videos	2018
30.	Theoretical Basis of Machine Learning	Lots of Legends, International Centre for Theoretical Sciences, TIFR	TBML-18	Lecture-Videos YouTube-Videos	2018

31.	Polish View on Machine Learning	Lots of Legends, Warsaw	PLinML-18	YouTube-Videos	2018
32.	Big Data Analysis in Astronomy	Lots of Legends, Tenerife	BDAA-18	YouTube-Lectures	2018
33.	Machine Learning Advances and Applications Seminar	Lots of Legends, Fields Institute, University of Toronto	MLASS	Video Lectures	2018-2019
34.	MIFODS- ML, Stats, ToC seminar	Lots of Legends, MIT	MIFODS-seminar	Lecture-videos	2018-2019
35.	Learning Machines Seminar Series	Lots of Legends, Cornell Tech	LMSS	YouTube-Lectures	2018-now
36.	Machine Learning Summer School	Lots of Legends, South Africa	MLSS'19	YouTube-Lectures	2019
37.	Deep Learning Boot Camp	Lots of Legends, Simons Institute, Berkeley	DLBC-19	YouTube-Lectures	2019
38.	Frontiers of Deep Learning	Lots of Legends, Simons Institute, Berkeley	FoDL-19	YouTube-Lectures	2019
39.	Mathematics of data: Structured representations for sensing, approximation and learning	Lots of Legends, The Alan Turing Institute, London	MoD-19	YouTube-Lectures	2019
40.	Deep Learning and Bayesian Methods	Lots of Legends, HSE Moscow	DLBM-SS	YouTube-Lectures	2019

41.	The Mathematics of Deep Learning and Data Science	Lots of Legends, Isaac Newton Institute, Cambridge	MoDL-DS	Lecture-Videos	2019
42.	Geometry of Deep Learning	Lots of Legends, MSR Redmond	GoDL	YouTube-Lectures	2019
43.	Deep Learning for Science School	Many folks, LBNL, Berkeley	DLfSS	YouTube-Lectures	2019
44.	Emerging Challenges in Deep Learning	Lots of Legends, Simons Institute, Berkeley	ECDL	YouTube-Lectures	2019
45.	Full Stack Deep Learning	Pieter Abbeel and many others, UC Berkeley	FSDL-M19	YouTube-Lectures-Day-1 Day-2	2019
46.	Algorithmic and Theoretical aspects of Machine Learning	Lots of legends, IIIT-Bengaluru	ACM-ML nptel	YouTube-Lectures	2019
47.	Deep Learning and Reinforcement Learning Summer School	Lots of Legends, AMII, Edmonton, Canada	DLRL-2019	YouTube-Lectures	2019
48.	Mathematics of Machine Learning - Summer Graduate School	Lots of Legends, University of Washington	MoML-SGS, MoML-SS	YouTube-Lectures	2019
49.	Workshop on Theory of Deep Learning: Where next?	Lots of Legends, IAS, Princeton University	WTDL	YouTube-Lectures	2019
50.	Computational Vision Summer School	Lots of Legends, Black Forest, Germany	CVSS-2019	YouTube-Lectures	2019

51.	Learning under complex structure	Lots of Legends, MIT	LUCS	YouTube-Lectures	2020
52.	Machine Learning Summer School	Lots of Legends, MPI-IS Tübingen (virtual)	MLSS	YouTube-Lectures	SS2020
53.	Eastern European Machine Learning Summer School	Lots of Legends, Kraków, Poland (virtual)	EEML	YouTube-Lectures	S2020
54.	Lisbon Machine Learning Summer School	Lots of Legends, Lisbon, Portugal (virtual)	LxMLS	YouTube-Lectures	S2020
55.	Workshop on New Directions in Optimization, Statistics and Machine Learning	Lots of Legends, Institute of Advanced Study, Princeton	ML-Opt new dir.	YouTube-Lectures	2020
56.	Mediterranean Machine Learning School	Lots of Legends, Italy (virtual)	M2L-school	YouTube-Lectures	2021
57.	Mathematics of Machine Learning - One World Seminar	Lots of Legends, Virtual	1W-ML	YouTube-Lectures	2020 - now
58.	Deep Learning Theory Summer School	Lots of Legends, Princeton University (virtual)	DLT'21	YouTube-Lectures	2021

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Bird's Eye view of A(G)I

S.No	Course Name	University/Instructor(s)	Course WebPage	Lecture Videos	Year
1.	Artificial General Intelligence	Lots of Legends, MIT	6.S099-AGI	Lecture-Videos	2018-2019
2.	AI Podcast	Lots of Legends, MIT	AI-Pod	YouTube-Lectures	2018-2019
3.	NYU - AI Seminars	Lots of Legends, NYU	modern-AI	YouTube-Lectures	2017-now
4.	Deep Learning: Alchemy or Science?	Lots of Legends, Institute for Advanced Study, Princeton	DLAS Agenda	YouTube-Lectures	2019

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" h5e = "

" # line break html lb_html = '
' # html url construction url1 = '' url3 = '' # center align something center1 = '

' center2 = '

' # navigation to toc g2c = "Go to Contents " divcont = '#contents' # navigation to top of table divdnns = '#dldnn' divmlfund = '#mlfund' divopt4ml = '#opt4ml' divgenml = '#genml' divreinf = '#reinf' divbdl = '#bayesdl' divmi = '#medimg' divpgm = '#probgm' divgnn = '#graphnn' divnlp = '#nlpnn' divasr = '#asrnn' divcvnn = '#cvnn' divbcss = '#bcss' divagi = '#aginn' def prettify_emoji(word): return em1 + word + em2 # down arrow down_arrow = ":arrow_heading_down:" down_arrow_prettified = prettify_emoji("arrow_heading_down") # up arrow up_arrow = ":arrow_heading_up:" up_arrow_prettified = prettify_emoji("arrow_heading_up") def parse_markdown_url(text): """ A simple helper function to convert markdwon url to a tuple: [url_text](https://) => ('url_text', 'https://') From: https://stackoverflow.com/a/23395483 """ # Anything that isn't a square closing bracket name_regex = "[^]]+" # http:// or https:// followed by anything but a closing paren url_regex = "http[s]?://[^)]+" markup_regex = '\[({0})]\(\s*({1})\s*\)'.format(name_regex, url_regex) extracted = [] for match in re.findall(markup_regex, text): extracted.append(match) return extracted # s = '| 1. | **Neural Networks for Machine Learning** | Geoffrey Hinton, University of Toronto | [Lecture-Slides](http://www.cs.toronto.edu/~hinton/coursera_slides.html)
[CSC321-tijmen](https://www.cs.toronto.edu/~tijmen/csc321/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoRl3Ht4JOcdU872GhiYWf6jwrk_SNhz9)
[UofT-mirror](https://www.cs.toronto.edu/~hinton/coursera_lectures.html) | 2012
2014 |' # print(parse_markdown_url(s)) def convert_markdown_url2html(text, extracted): if len(extracted) == 1: url_text, url_link = extracted[0][0], extracted[0][1] text = text.replace(url_text, "") text = text.replace(url_link, "") url = url1 + url_link + url2 + url_text + url3 text = text.replace("[]()", url) # center align text = center1 + text + center2 return text def table_topic_emoji_processor(line): if ":" in line: matches = re.findall(r":(.*?):", line) # print(matches) for emj in matches: m = ":" + emj + ":" if m in line: # line = line.replace(m, prettify_emoji(emj)) # uncomment this line to enable emojis line = line.replace(m, '') return line.strip() def add_navigation_button(): """ adds a go to contents button """ pret = prettify_emoji("arrow_heading_up") pret = url1 + host_url + host_page + divcont + url2 + h5b + g2c + pret + h5e + url3 return pret def replace_github_url_with_webpage_url(line, matches): for lnk in matches: lnk = lnk.replace('(', '') lnk = lnk.replace(')', '') if "deep-learning-deep-neural-networks" in line: line = line.replace(lnk, host_url + host_page + divdnns) if "probabilistic-graphical-models" in line: line = line.replace(lnk, host_url + host_page + divpgm) if "bayesian-deep-learning" in line: line = line.replace(lnk, host_url + host_page + divbdl) if "medical-imaging" in line: line = line.replace(lnk, host_url + host_page + divmi) if "machine-learning-fundamentals" in line: line = line.replace(lnk, host_url + host_page + divmlfund) if "natural-language-processing" in line: line = line.replace(lnk, host_url + host_page + divnlp) if "optimization-for-machine-learning" in line: line = line.replace(lnk, host_url + host_page + divopt4ml) if "automatic-speech-recognition-speech" in line: line = line.replace(lnk, host_url + host_page + divasr) if "general-machine-learning" in line: line = line.replace(lnk, host_url + host_page + divgenml) if "modern-computer-vision" in line: line = line.replace(lnk, host_url + host_page + divcvnn) if "reinforcement-learning" in line: line = line.replace(lnk, host_url + host_page + divreinf) if "boot-camps-or-summer-schools" in line: line = line.replace(lnk, host_url + host_page + divbcss) if "graph-neural-networks" in line: line = line.replace(lnk, host_url + host_page + divgnn) if "birds-eye-view-of-agi" in line: line = line.replace(lnk, host_url + host_page + divagi) return line # desired html with open('index.html', 'w') as f: # write header info f.write(''.join(html_head)) heavy_minus_tracker = 0 all_lines = [] table_of_contents = [] dl_dnn = [] ml_fund = [] opt_ml = [] gen_ml = [] reinf_learn = [] bayes_dl = [] med_img = [] prob_gm = [] graph_nn = [] nlp_nn = [] asr_nn = [] cv_nn = [] bcss = [] agi_nn = [] for line in markdn: if line.startswith('# '): line = line.replace('# ', '') # very first line if ":" in line: temp = [] line_contents = line.split(" ") for w in line_contents: if ":" in w: continue # comment this line to enable emoji w = w.replace(":", "") w = prettify_emoji(w) temp.append(w) else: temp.append(w) line = " ".join(temp) del temp line = h1b + fontawesome_animations[0] + line + fontawesome_animations[1] + h1e all_lines.append(line) # line separator all_lines.append('

') # quote if ":books:" in line: # extract URL/text extracted = parse_markdown_url(line) line = convert_markdown_url2html(line, extracted) # line = line.replace(lb_html, '') # remove line break line = line.replace(":books:", prettify_emoji("books")) # color code emoji line = line.replace('**"', '"') # bold text line = line.replace('"**', '"') all_lines.append(line) # if heavy minus, just pass if ":heavy_minus_sign:" in line: heavy_minus_tracker += 1 continue # table of contents if "### Contents" in line: line = line.replace('### ', '') line = '

' + ahref1 + host_page + divcont + ahrefm + h2b + line + h2e + ahref2 line = line + '

' # print(line) all_lines.append(line) # group toc in a list and then process them if "| " in line and heavy_minus_tracker == 2: line = line.replace(down_arrow, down_arrow_prettified) extr = parse_markdown_url(line) matches = re.findall(r"\(https.*?\)", line) line = replace_github_url_with_webpage_url(line, matches) table_of_contents.append(line) # signifies end of table; now convert them to html table if heavy_minus_tracker == 3: # convert toc markdown to html table toc_html = markdown.markdown("".join(table_of_contents), extensions=['markdown.extensions.tables']) toc_html = toc_html.replace('', '

') # center align table all_lines.append(toc_html) all_lines.append('

') all_lines.append('

') # Depp Neurl Networks TABLE # table Deep Learning (Deep Neural Networks) if "## :tada: Deep Learning" in line: line = line.replace('## ', '') line = table_topic_emoji_processor(line) line = ahref1 + host_page + divdnns + ahrefm + h2b + line + h2e + ahref2 # print(line) all_lines.append(line) # group DNN table in a list and then process them if "| " in line and heavy_minus_tracker == 4: dl_dnn.append(line) # signifies end of table; now convert them to html table if heavy_minus_tracker == 5: # convert DL DNN markdown to html table dl_dnn_html = markdown.markdown("".join(dl_dnn), extensions=['markdown.extensions.tables']) dl_dnn_html = dl_dnn_html.replace('

', '

') # center align table # remove underline in url links dl_dnn_html = dl_dnn_html.replace('">', '" style="text-decoration:none">') # print(dl_dnn_html) all_lines.append(dl_dnn_html) # navigation to top all_lines.append(add_navigation_button()) all_lines.append("

") # ML FUNDAMENTALS TABLE # ML fundamentals if "### :cupid: Machine Learning" in line: line = line.replace('### ', '') line = table_topic_emoji_processor(line) line = ahref1 + divmlfund + ahrefm + h2b + line + h2e + ahref2 # print(line) all_lines.append(line) # group ML fundamentals table in a list and then process them if "| " in line and heavy_minus_tracker == 6: ml_fund.append(line) # signifies end of table; now convert them to html table if heavy_minus_tracker == 7: # convert ML Funda markdown to html table ml_fund_html = markdown.markdown("".join(ml_fund), extensions=['markdown.extensions.tables']) ml_fund_html = ml_fund_html.replace('

', '

') # center align table # remove underline in url links ml_fund_html = ml_fund_html.replace('">', '" style="text-decoration:none">') # print(ml_fund_html) all_lines.append(ml_fund_html) # navigation to top all_lines.append(add_navigation_button()) all_lines.append("

") # OPT for ML TABLE # Optimization for Machine Learning if "### :cupid: Optimization for Machine Learning" in line: line = line.replace('### ', '') line = table_topic_emoji_processor(line) line = ahref1 + divopt4ml + ahrefm + h2b + line + h2e + ahref2 # print(line) all_lines.append(line) # group OPT 4 ML table in a list and then process them if "| " in line and heavy_minus_tracker == 8: opt_ml.append(line) # signifies end of table; now convert them to html table if heavy_minus_tracker == 9: # convert Optim 4 ML markdown to html table opt_ml_html = markdown.markdown("".join(opt_ml), extensions=['markdown.extensions.tables']) opt_ml_html = opt_ml_html.replace('

', '

') # center align table # remove underline in url links opt_ml_html = opt_ml_html.replace('">', '" style="text-decoration:none">') # print(opt_ml_html) all_lines.append(opt_ml_html) # navigation to top all_lines.append(add_navigation_button()) all_lines.append("

") # GENERAL ML TABLE # General Machine Learning if "### :cupid: General Machine" in line: line = line.replace('### ', '') line = table_topic_emoji_processor(line) line = ahref1 + divgenml + ahrefm + h2b + line + h2e + ahref2 # print(line) all_lines.append(line) # group General ML table in a list and then process them if "| " in line and heavy_minus_tracker == 10: gen_ml.append(line) # signifies end of table; now convert them to html table if heavy_minus_tracker == 11: # convert General ML markdown to html table gen_ml_html = markdown.markdown("".join(gen_ml), extensions=['markdown.extensions.tables']) gen_ml_html = gen_ml_html.replace('

', '

') # center align table # remove underline in url links gen_ml_html = gen_ml_html.replace('">', '" style="text-decoration:none">') # print(gen_ml_html) all_lines.append(gen_ml_html) # navigation to top all_lines.append(add_navigation_button()) all_lines.append("

") # REINFORCEMENT LEARNING TABLE # Reinforcement Learning if "### :balloon: Reinforcement" in line: line = line.replace('### ', '') line = table_topic_emoji_processor(line) line = ahref1 + divreinf + ahrefm + h2b + line + h2e + ahref2 # print(line) all_lines.append(line) # group Reinforcement Learning table in a list and then process them if "| " in line and heavy_minus_tracker == 12: reinf_learn.append(line) # signifies end of table; now convert them to html table if heavy_minus_tracker == 13: # convert reinforce learn markdown to html table reinf_learn_html = markdown.markdown("".join(reinf_learn), extensions=['markdown.extensions.tables']) reinf_learn_html = reinf_learn_html.replace('

', '

') # center align table # remove underline in url links reinf_learn_html = reinf_learn_html.replace('">', '" style="text-decoration:none">') # print(reinf_learn_html) all_lines.append(reinf_learn_html) # navigation to top all_lines.append(add_navigation_button()) all_lines.append("

") # PROBABILISTIC GRAPHICAL MODELS TABLE # Probabilistic Graphical Models if "### :loudspeaker: Probabilistic Graphical Models" in line: line = line.replace('### ', '') line = table_topic_emoji_processor(line) line = ahref1 + divpgm + ahrefm + h2b + line + h2e + ahref2 # print(line) all_lines.append(line) # group Probabilistic Graphical Models table in a list and then process them if "| " in line and heavy_minus_tracker == 14: prob_gm.append(line) # signifies end of table; now convert them to html table if heavy_minus_tracker == 15: # convert Probabilistic Graphical Models markdown to html table prob_gm_html = markdown.markdown("".join(prob_gm), extensions=['markdown.extensions.tables']) prob_gm_html = prob_gm_html.replace('

', '

') # center align table # remove underline in url links prob_gm_html = prob_gm_html.replace('">', '" style="text-decoration:none">') # print(prob_gm_html) all_lines.append(prob_gm_html) # navigation to top all_lines.append(add_navigation_button()) all_lines.append("

") # BAYESIAN DEEP LEARNING TABLE # Bayesian Deep Learning if "## :game_die: Bayesian" in line: line = line.replace('## ', '') line = table_topic_emoji_processor(line) line = ahref1 + divbdl + ahrefm + h2b + line + h2e + ahref2 # print(line) all_lines.append(line) # group Bayesian Deep Learning table in a list and then process them if "| " in line and heavy_minus_tracker == 16: bayes_dl.append(line) # signifies end of table; now convert them to html table if heavy_minus_tracker == 17: # convert bayes deep learn markdown to html table bayes_dl_html = markdown.markdown("".join(bayes_dl), extensions=['markdown.extensions.tables']) bayes_dl_html = bayes_dl_html.replace('

', '

') # center align table # remove underline in url links reinf_learn_html = bayes_dl_html.replace('">', '" style="text-decoration:none">') # print(bayes_dl_html) all_lines.append(bayes_dl_html) # navigation to top all_lines.append(add_navigation_button()) all_lines.append("

") # MEDICAL IMAGING TABLE # Medical Imaging if "## :movie_camera: Medical" in line: line = line.replace('## ', '') line = table_topic_emoji_processor(line) line = ahref1 + divmi + ahrefm + h2b + line + h2e + ahref2 # print(line) all_lines.append(line) # group Medical Imaging table in a list and then process them if "| " in line and heavy_minus_tracker == 18: med_img.append(line) # signifies end of table; now convert them to html table if heavy_minus_tracker == 19: # convert medical imaging markdown to html table med_img_html = markdown.markdown("".join(med_img), extensions=['markdown.extensions.tables']) med_img_html = med_img_html.replace('

', '

') # center align table # remove underline in url links med_img_html = med_img_html.replace('">', '" style="text-decoration:none">') # print(med_img_html) all_lines.append(med_img_html) # navigation to top all_lines.append(add_navigation_button()) all_lines.append("

") # GRAPH NEURAL NETWORKS TABLE # Graph Neural Networks if "## :tada: Graph Neural Networks" in line: line = line.replace('## ', '') line = table_topic_emoji_processor(line) line = ahref1 + divgnn + ahrefm + h2b + line + h2e + ahref2 # print(line) all_lines.append(line) # group Graph Neural Networks table in a list and then process them if "| " in line and heavy_minus_tracker == 20: graph_nn.append(line) # signifies end of table; now convert them to html table if heavy_minus_tracker == 21: # convert Graph Neural Networks markdown to html table graph_nn_html = markdown.markdown("".join(graph_nn), extensions=['markdown.extensions.tables']) graph_nn_html = graph_nn_html.replace('

', '

') # center align table # remove underline in url links graph_nn_html = graph_nn_html.replace('">', '" style="text-decoration:none">') # print(graph_nn_html) all_lines.append(graph_nn_html) # navigation to top all_lines.append(add_navigation_button()) all_lines.append("

") # NATURAL LANGUAGE PROCESSING TABLE # Natural Language Processing if "### :hibiscus: Natural Language Processing" in line: line = line.replace('### ', '') line = table_topic_emoji_processor(line) line = ahref1 + divnlp + ahrefm + h2b + line + h2e + ahref2 # print(line) all_lines.append(line) # group Natural Language Processing table in a list and then process them if "| " in line and heavy_minus_tracker == 22: nlp_nn.append(line) # signifies end of table; now convert them to html table if heavy_minus_tracker == 23: # convert Graph Neural Networks markdown to html table nlp_nn_html = markdown.markdown("".join(nlp_nn), extensions=['markdown.extensions.tables']) nlp_nn_html = nlp_nn_html.replace('

', '

') # center align table # remove underline in url links nlp_nn_html = nlp_nn_html.replace('">', '" style="text-decoration:none">') # print(nlp_nn_html) all_lines.append(nlp_nn_html) # navigation to top all_lines.append(add_navigation_button()) all_lines.append("

") # Automatic Speech Recognition TABLE # Automatic Speech Recognition if "### :speaking_head: Automatic Speech Recognition" in line: line = line.replace('### ', '') line = table_topic_emoji_processor(line) line = ahref1 + divasr + ahrefm + h2b + line + h2e + ahref2 # print(line) all_lines.append(line) # group Automatic Speech Recognition table in a list and then process them if "| " in line and heavy_minus_tracker == 24: asr_nn.append(line) # signifies end of table; now convert them to html table if heavy_minus_tracker == 25: # convert Automatic Speech Recognition markdown to html table asr_nn_html = markdown.markdown("".join(asr_nn), extensions=['markdown.extensions.tables']) asr_nn_html = asr_nn_html.replace('

', '

') # center align table # remove underline in url links asr_nn_html = asr_nn_html.replace('">', '" style="text-decoration:none">') # print(asr_nn_html) all_lines.append(asr_nn_html) # navigation to top all_lines.append(add_navigation_button()) all_lines.append("

") # Modern Computer Vision TABLE # Modern Computer Vision if "### :fire: Modern Computer Vision" in line: line = line.replace('### ', '') line = table_topic_emoji_processor(line) line = ahref1 + divcvnn + ahrefm + h2b + line + h2e + ahref2 # print(line) all_lines.append(line) # group Modern Computer Vision table in a list and then process them if "| " in line and heavy_minus_tracker == 26: cv_nn.append(line) # signifies end of table; now convert them to html table if heavy_minus_tracker == 27: # convert Modern Computer Vision markdown to html table cv_nn_html = markdown.markdown("".join(cv_nn), extensions=['markdown.extensions.tables']) cv_nn_html = cv_nn_html.replace('

', '

') # center align table # remove underline in url links cv_nn_html = cv_nn_html.replace('">', '" style="text-decoration:none">') # print(cv_nn_html) all_lines.append(cv_nn_html) # navigation to top all_lines.append(add_navigation_button()) all_lines.append("

") # Boot Camps or Summer Schools TABLE # Boot Camps or Summer Schools if "### :star2: Boot Camps or Summer Schools" in line: line = line.replace('### ', '') line = table_topic_emoji_processor(line) line = ahref1 + divbcss + ahrefm + h2b + line + h2e + ahref2 # print(line) all_lines.append(line) # group Boot Camps or Summer Schools table in a list and then process them if "| " in line and heavy_minus_tracker == 28: bcss.append(line) # signifies end of table; now convert them to html table if heavy_minus_tracker == 29: # convert Boot Camps or Summer Schools markdown to html table bcss_html = markdown.markdown("".join(bcss), extensions=['markdown.extensions.tables']) bcss_html = bcss_html.replace('

', '

') # center align table # remove underline in url links bcss_html = bcss_html.replace('">', '" style="text-decoration:none">') # print(bcss_html) all_lines.append(bcss_html) # navigation to top all_lines.append(add_navigation_button()) all_lines.append("

") # Bird's Eye view of A(G)I TABLE # Bird's Eye view of A(G)I if "### :bird: Bird" in line: line = line.replace('### ', '') line = table_topic_emoji_processor(line) line = ahref1 + divagi + ahrefm + h2b + line + h2e + ahref2 # print(line) all_lines.append(line) # group Bird's Eye view of A(G)I table in a list and then process them if "| " in line and heavy_minus_tracker == 30: agi_nn.append(line) # signifies end of table; now convert them to html table if heavy_minus_tracker == 31: # convert Bird's Eye view of A(G)I markdown to html table agi_nn_html = markdown.markdown("".join(agi_nn), extensions=['markdown.extensions.tables']) agi_nn_html = agi_nn_html.replace('

', '

') # center align table # remove underline in url links agi_nn_html = agi_nn_html.replace('">', '" style="text-decoration:none">') # print(agi_nn_html) all_lines.append(agi_nn_html) # navigation to top all_lines.append(add_navigation_button()) all_lines.append("

") # write everything to desired html file for line in all_lines: f.write(line) # write footer info f.write(''.join(html_foot))