├── LICENSE └── readme.md /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 Zack Chase Lipton 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /readme.md: -------------------------------------------------------------------------------- 1 | # Machine Learning Resources 2 | 3 | ## Introductory Textbooks (welcoming primers) 4 | * [*Machine Learning*](https://www.amazon.com/Machine-Learning-Tom-M-Mitchell/dp/1259096955/), Tom Mitchell—a very readable intro to ML book. Surprisingly relevant today even after 20-30 years. Starts simple with discrete data, finite hypothesis classes, inductive bias, an intuitive exposure to no free lunch. Works through decision trees, neural networks (perhaps the most dates aspect), basic statistics, a high-level exposure to PAC-learning. Scores major points as an intro text for broad scope, conversational tone, readability. 5 | [[Amazon](https://www.amazon.com/Machine-Learning-Tom-M-Mitchell/dp/1259096955/ref=pd_lpo_sbs_14_t_0?_encoding=UTF8&psc=1&refRID=PTJX3DFHDCVA314621WB)] 6 | [[Free](http://profsite.um.ac.ir/~monsefi/machine-learning/pdf/Machine-Learning-Tom-Mitchell.pdf)] 7 | 8 | 9 | ## Boad Machine Learning Textbooks (for familiarized readers) 10 | * [*Pattern Recognition and Machine Learning*](https://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738), Christopher Bishop—Probably not the textbook to try to chug through when you're new to the field (will have to survive 100-200 pages before getting to your first practical model and not hands-on, but perhaps the best second textbook, for someone who already knows enough to be dangerous but wants to develop a more rigorous understanding. 11 | [[Amazon](https://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738)] 12 | [[Free](http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf)] 13 | 14 | ## By Topic 15 | 16 | ### Basic Probability and Stats 17 | * [*Seeing Theory* (from Brown) visual/interactive tutorial on basic probability](https://seeing-theory.brown.edu/basic-probability/index.html) 18 | 19 | ### Information Theory 20 | * [*A Mathematical Theory of Communication*](http://math.harvard.edu/~ctm/home/text/others/shannon/entropy/entropy.pdf), 21 | Claude Shannon—the foundational paper in the area, still reads crystal clear. 22 | 23 | ### Support Vector Machines 24 | * [Patrick Winston's lecture from MIT OpenCourseWare](https://www.youtube.com/watch?v=_PwhiWxHK8o) 25 | 26 | ### Probabilistic Graphical Models 27 | * [*Probabilistic Reasoning in Intelligent Systems*](https://www.amazon.com/Probabilistic-Reasoning-Intelligent-Systems-Representation/dp/1558604790), Judea Pearl—The classic text on PGMs. Great for getting a feeling of where the ideas came from and to see how they were argued for in a pre-probabilistic AI community but be careful, a lot has changed since 1988. 28 | * [Mathematical Monk's explanation of the forward-backward algorithm for HMMs](https://www.youtube.com/watch?v=7zDARfKVm7s) 29 | 30 | ### Deep Learning 31 | 32 | ### Learning Theory 33 | * [*Understanding Machine Learning*](https://www.amazon.com/Understanding-Machine-Learning-Theory-Algorithms/dp/1107057132), Shai Shalev-Schwartz and Shai Ben-David—great textbook introducing key ideas of PAC-learning, VC-dimension. 34 | 35 | ### Reinforcement Learning 36 | * [*Reinforcement Learning: An Introduction*](https://www.amazon.com/Reinforcement-Learning-Introduction-Adaptive-Computation/dp/0262193981) 37 | by Sutton and Barto—the definitive introductory text in reinforcement learning. Easy to read and few prerequisites required. Long on intuition and short on maths, a perfect first exposure to the topic. 38 | [[Amazon](https://www.amazon.com/Reinforcement-Learning-Introduction-Adaptive-Computation/dp/0262193981)] 39 | [[Free (Draft of 2nd Ed.)](http://incompleteideas.net/book/bookdraft2017nov5.pdf)] 40 | * [David Silver's UCL RL Course Lectures](https://www.youtube.com/watch?v=lfHX2hHRMVQ&list=PLzuuYNsE1EZAXYR4FJ75jcJseBmo4KQ9-&index=2)—Starts at the very beginning. What is reinforcement learning? How does it fit with other related disciplines. Walks through basics of Markov processes (MPs), Markov Reward Processes (MRPs), Markov Decision Processes (MDPs). Introduces basics of policy evaluation and improvement in tabular case: value iteration, policy iteration, TD learning, MC rollouts, Q-learning, SARSA. Then goes into function approximation and the familiar modern tools of DQNs, policy gradient, actor critic, etc. Worderfully delivered. 41 | 42 | 43 | ## Possible organization(?): 44 | *Within each topic, sub bullet by type of resource / flag types for (i) books, (ii) lecture notes, (iii) videos, (iv) code, (v)blogposts, (vi) foundational/historical papers* 45 | --------------------------------------------------------------------------------