Repository: sangwoomo/ml-resources Branch: master Commit: e4340613865a Files: 1 Total size: 10.5 KB Directory structure: gitextract_zrdqmkiw/ └── README.md ================================================ FILE CONTENTS ================================================ ================================================ FILE: README.md ================================================ # Some resources for ML research Personal and biased selection of ML resources. **Disclaimer:** I'm a noivce in ML research, and I read only a few of the list. ## Table of Contents - [Beginner's Guide](#beginners-guide) - [Machine Learning](#machine-learning) - [Deep Learning](#deep-learning) - [Generative Model](#generative-model) - [Reinforcement Learning](#reinforcement-learning) - [Graphical Model](#graphical-model) - [Optimization](#optimization) - [Learning Theory](#learning-theory) - [Statistics](#statistics) - [Topics in Machine Learning](#topics-in-machine-learning) - [Math Backgrounds](#math-backgrounds) - [Blogs](#blogs) ## Beginner's Guide **Must Read** - Machine Learning: A Probabilistic Perspective (Murphy) - Deep Learning (Goodfellow et al.) - Reinforcement Learning: An Introduction (Sutton & Barto) **Recommended** - Convex Optimization (Boyd & Vandenberghe) - Graphical Models, Exponential Families, and Variational Inference (Wainwright & Jordan) - Learning from Data (Abu-Mostafa) *-> for whom interested in learning theory* **Recent Topics** - Read research blogs (e.g., [OpenAI](https://blog.openai.com/), [BAIR](http://bair.berkeley.edu/blog/), [CMU](https://blog.ml.cmu.edu/)) - Read lectures from Berkeley, Stanford, CMU or UofT (e.g., [unsupervised learning](https://sites.google.com/view/berkeley-cs294-158-sp19)) - There are lots of good sources, but I stop updating them up-to-date ## Machine Learning There are many ML books, but most of them are encyclopedic. <br/> I recommend to take a course using Murphy or Bishop book. ### Textbook - Machine Learning: A Probabilistic Perspective (Murphy) :sparkles: - Pattern Recognition and Machine Learning (Bishop) :sparkles: - The Elements of Statistical Learning (Hastie et al.) - Pattern Classification (Duda et al.) - Bayesian Reasoning and Machine Learning (Barber) ### Lecture - [Stanford CS229 Machine Learning](http://cs229.stanford.edu) :sparkles: - [CMU 10701 Mahine Learning](http://www.cs.cmu.edu/~tom/10701_sp11/) - [CMU 10702 Statistical Machine Learning](http://www.stat.cmu.edu/~larry/=sml/) - [Oxford Machine Learning](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/) ## Deep Learning Goodfellow et al. is the new classic. <br/> For vision and NLP, Stanford lectures would be helpful. ### Textbook - Deep Learning (Goodfellow et al.) :sparkles: ### Lecture (Practice) - [Deep Learning book](http://www.deeplearningbook.org/lecture_slides.html) :sparkles: - [Stanford CS231n Convolutional Neural Networks for Visual Recognition](http://cs231n.stanford.edu) :sparkles: - [Stanfrod CS224d Deep Learning for Natural Language Processing](http://cs224d.stanford.edu) - [Stanfrod CS224s Spoken Language Processing](http://web.stanford.edu/class/cs224s/) - [Oxford Deep Natural Language Processing](https://github.com/oxford-cs-deepnlp-2017/lectures) - [CMU 11747 Neural Networks for NLP](http://phontron.com/class/nn4nlp2017/index.html) ### Lecture (Theory) - [Stanford Stat385 Theories of Deep Learning](https://stats385.github.io/) ## Generative Model I seperated generative model as an independent topic, <br/> since I think it is big and important area. ### Lecture - [Toronto CSC2541 Differentiable Inference and Generative Models](https://www.cs.toronto.edu/~duvenaud/courses/csc2541/index.html) - [Toronto CSC2547 Learning Discrete Latent Structure](https://duvenaud.github.io/learn-discrete/) - [Toronto CSC2541 Scalable and Flexible Models of Uncertainty](https://csc2541-f17.github.io/) ## Reinforcement Learning For classic (non-deep) RL, Sutton & Barto is the classic. <br/> For deep RL, lectures from Berkeley/CMU looks good. ### Textbook - Reinforcement Learning: An Introduction (Sutton & Barto) :sparkles: ### Lecture - [UCL Reinforcement Learning](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html) :sparkles: - [Berkeley CS294 Deep Reinforcement Leanring](http://rll.berkeley.edu/deeprlcourse/) :sparkles: - [CMU 10703 Deep Reinforcement Learing and Control](https://katefvision.github.io/) ## Graphical Model Koller & Friedman is comprehensive, but too encyclopedic. <br/> I recommend to take an introductory course using Koller & Friedman book. <br/> Wainwright & Jordan only focuses on variational inference, <br/> but it gives really good intuition for probabilistic models. ### Textbook - Probabilistic Graphical Models: Principles and Techniques (Koller & Friedman) - Graphical Models, Exponential Families, and Variational Inference (Wainwright & Jordan) :sparkles: ### Lecture - [Stanford CS228 Probabilistic Graphical Models](http://cs.stanford.edu/~ermon/cs228/index.html) - [CMU 10708 Probabilistic Graphical Models](http://www.cs.cmu.edu/~epxing/Class/10708/) :sparkles: ## Optimization Boyd & Vandenberghe is the classic, but I think it's too boring. <br/> Reading chapter 2-5 would be enough. Bertsekas more concentrates on convex analysis. <br/> Nocedal & Wright more concentrates on optimization. ### Textbook - Convex Optimization (Boyd & Vandenberghe) :sparkles: - Convex Optimization Theory (Bertsekas) - Numerical Optimization (Nocedal & Wright) ### Lecture - [Stanford EE364a Convex Optimization I](http://stanford.edu/class/ee364a/) :sparkles: - [Stanford EE364b Convex Optimization II](http://stanford.edu/class/ee364a/) - [MIT 6.253 Convex Analysis and Optimization](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-253-convex-analysis-and-optimization-spring-2012/index.htm) ## Learning Theory In my understanding, there are two major topics in learning theory: - **Learning Theory:** VC-dimension, PAC-learning - **Online Learning:** regret bound, multi-armed bandit For learning theory, Kearns & Vazirani is the classic; but it's too old-fashined. <br/> Abu-Mostafa is a good introductory book, and I think it's enough for most people. For online learning, Cesa-Bianchi & Lugosi is the classic. <br/> For multi-armed bandit, Bubeck & Cesa-Bianchi provides a good survey. ### Textbook (Learning Theory) - Learning from Data (Abu-Mostafa) :sparkles: - Foundations of Machine Learning (Mohri et al.) - An Introduction to Computational Learning Theory (Kearns & Vazirani) ### Textbook (Online Learning) - Prediction, Learning, and Games (Cesa-Bianchi & Lugosi) - Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems (Bubeck & Cesa-Bianchi) ### Lecture - [Caltech Learning from Data](https://work.caltech.edu/telecourse.html) :sparkles: - [CMU 15859 Machine Learning Theory](http://www.cs.cmu.edu/~avrim/ML14/) - [Berkeley CS281b/Stat241b Statistical Learning Theory](https://www.stat.berkeley.edu/~bartlett/courses/2014spring-cs281bstat241b/) - [MIT 9.520 Statistical Learning Theory and Applications](http://www.mit.edu/~9.520/fall15/) ## Statistics Statistics is a broad area; hence, I listed only a few of them. <br/> For advanced topics, lectures from Berkeley/Stanford/CMU/MIT looks really cool. <br/> ### Textbook (Statistical Inference) - All of Statistics (Wasserman) - Computer Age Statistical Inference (Efron & Hastie) :sparkles: - Time Series Analysis and Its Applications: With R Examples (Shumway & Stoffer) ### Textbook (Nonparametrics) - All of Nonparametric Statistics (Wasserman) - Introduction to Nonparametric Estimation (Tsybakov) - Gaussian Process and Machine Learning (Rasmussen & Williams) :sparkles: - Bayesian Nonparametrics (Ghosh & Ramamoorthi) :sparkles: ### Textbook (Advanced Topics) - High-Dimensional Statistics: A Non-Asymptotic Viewpoint (Wainwright) :sparkles: - Statistics for High-Dimensional Data (Bühlmann & van de Geer) - Asymptotic Statistics (van der Vaart) - Empirical Processes in M-Estimation (van der Vaart) ### Lecture - [Berkeley Stat210a Theoretical Statistics I](https://www.stat.berkeley.edu/~wfithian/courses/stat210a/) - [Berkeley Stat210b Theoretical Statistics II](https://people.eecs.berkeley.edu/~jordan/courses/210B-spring17/) - [Stanford Stat300a Theory of Statistics](https://web.stanford.edu/~lmackey/stats300a/) - [Stanford CS369m Algorithms for Massive Data Set Analysis](http://cs.stanford.edu/people/mmahoney/cs369m/) - [CMU 36755 Advanced Statistical Theory I](http://www.stat.cmu.edu/~arinaldo/36755/F16/) - [MIT 18.S997 High-Dimensional Statistics](https://ocw.mit.edu/courses/mathematics/18-s997-high-dimensional-statistics-spring-2015/) ## Topics in Machine Learning Miscellaneous topics related to machine learning. <br/> There are much more subfields, but I'll not list them all. ### Information Theory - Elements of Information Theory (Cover & Thomas) - Information Theory, Inference, and Learning Algorithms (MacKay) ### Network Science - Networks, Crowds, and Markets (Easley & Kleinberg) - Social and Economic Networks (Jackson) ### Markov Chain - Markov Chains (Norris) - Markov Chains and Mixing Times (Levin et al.) ### Game Theory - Algorithmic Game Theory (Nisan et al.) - Multiagent Systems (Shoham & Leyton-Brown) ### Combinatorics - The Probabilistic Method (Alon & Spencer) - A First Course in Combinatorial Optimization (Lee) ### Algorithm - Introduction to Algorithms (Cormen et al.) - Randomized Algorithms (Motwani & Raghavan) - Approximation Algorithms (Vazirani) ### Geometric View - Topological Data Analysis (Wasserman) - Methods of Information Geometry (Amari & Nagaoka) - Algebraic Geometry and Statistical Learning Theory (Watanabe) ### Some Lectures - [MIT 18.409 Algorithmic Aspects of Machine Learning](http://people.csail.mit.edu/moitra/409.html) - [MIT 18.409 An Algorithmist's Toolkit](http://stellar.mit.edu/S/course/18/fa09/18.409/) ## Math Backgrounds I selected essential topics for machine learning. <br/> Personally, I think more analysis / matrix / geometry never hurts. ### Probability - Probability: Theory and Examples (Durrett) - Theoretical Statistics (Keener) - Stochastic Processes (Bass) - Probability and Statistics Cookbook (Vallentin) ### Linear Algebra - Linear Algebra (Hoffman & Kunze) - Matrix Analysis (Horn & Johnson) - Matrix Computations (Golub & Van Loan) - The Matrix Cookbook (Petersen & Pedersen) ### Large Deviations - Concentration Inequalities and Martingale Inequalities (Chung & Lu) - An Introduction to Matrix Concentration Inequalities (Tropp) ## Blogs - [Google AI Blog](https://ai.googleblog.com/) - [DeepMind Blog](https://deepmind.com/blog/?category=research) - [OpenAI Blog](https://blog.openai.com/) - [FAIR Blog](https://research.fb.com/blog/) - [Distill.pub](https://distill.pub/) - [BAIR Blog](http://bair.berkeley.edu/blog/) - [CMU Blog](https://blog.ml.cmu.edu/) - [Off the convex path](http://www.offconvex.org/) - [inFERENCe](http://www.inference.vc/) - [Sebastian Ruder](http://ruder.io/#open) - [Lunit Tech Blog (Korean)](https://blog.lunit.io/category/paper-review/)
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