Resources
Latest updated: Jan 2, 2024
Research utilities
Books/Survey
- Harvard CS197 - AI research experiences
- Stanford CS197 - Computer Science research
- CS Research 101 by Neeldhara Misra and Shashank Srikant
- How to read research papers by Aaditya Ramdas
- AI research journey and advice by Jason Wei
- You and Your research by Richard Hamming
- A few words on research for graduate students by Fan Chung Graham
- Four golden lessons by Steven Weinberg
- How I prepared for Deepmind and google AI research internship interviews in 2019 by David Stutz
- Tips for Success as a New Researcher by Alex Tamkin
- Research as a Stochastic Decision Process by Jacob Steinhardt
- Ph.D students must break away from undergraduate mentality by Jason Hong
- Research Taste Exercises by Christopher Olah
- Better Saving and Logging for Research experiments by Daniel Seita
- CS PhD Statements of Purpose
- Personal Statement Advice by Suchin Gururangan
- PhD Statement of Purpose by Nelson Liu
- Writing by Eric Zhang
- AI paper Feed
- Tips for Writing Technical Papers by Jennifer Widom
- How to avoid ML pitfalls: a guide for academic researchers by Michael A. Lones
- Write the paper first by Jason Eisner
- An opinionated guide to ML research by John Schulman
- Lessons learned the hard way in grad school (so far) by Andrey Kurenkov
- De-Mystifying Good Research and Good Papers by Fei-Fei Li
- What You Know Matters More Than What You Do by Cal Newport
- Collaboration and Credit principles by Christopher Olah
- The PhD Grind — Philip Guo’s E-book bu Daniel Takeshi
- SOP - Rishabh Ranjan
- SOP - Ameya Daigavane
- SOP - Siddartha Devic
- SOP - Aaron Dharna
- SOP - Naveen Raman
- 3 qualities of successful Ph.D. students; Perseverance, tenacity and cogency by Matt Might
- Console productivity hack: Discover the frequent; then make it the easy by Matt Might
- Classroom Fortress: The Nine Kinds of Students by Matt Might
- 10 easy ways to fail a Ph.D. by Matt Might
- Productivity tips, tricks and hacks for academics (2015 edition) by Matt Might
- What every computer science major should know by Matt Might
- Fan Pu Zeng
- Troubling trends in machine learning scholarship by Zachary Lipton, Jacob Steinhardt
- Career examples: proposals+comments
- How to organize your files by Jason Eisner
- How to find research problems by Jason Eisner
- 3 shell scripts to improve your writing, or “My Ph.D. advisor rewrote himself in bash by Matt Might
- Problem-Solving strategies by Arthur Engel
Links
- Tuning playbook
- Jia-Huang Bin research advice
- ML Contests
- Arxiv-sanity
- Graduate Fellowship Opportunities 2023-24 Academic Year
- Applied ML
- How to Do Great Research
Bloggers
- Sebastian Ruder - NLP-focused
- Lil’Log: Many introductions to new topics, well-written, easy to understand, highly recommend!
- Jay Alammar: Many introductions to new topics, good visualizations
- Andrej Karpathy: Legends in the NLP, many good tips
- Distill Many introductions to new topics, good visualizations
- Colah’s blog: Focus on computer vision, many good tips, collaboration with Distill.
- I’m a bandit: Optimization, statistics, probability theory, ML theory.
- Sudeep Raja: Online learning
- Bounded Regret: Introductions to new topics, many opinions, many tips
- inFERENCe: Statistics, various topics, many tutorials, insights and opinions
- Mike Bostock: Software tips
- Michael Nielsen: Focused on CV, touch on quantum.
- Daniel Takeshi’s blog Various introductory topics, class reviews @Berkeley, lots of tips
- Gregory Gundersen: Focused on statistics, time-series data, GP, Bayesian inference
- Massimiliano (Max) Patacchiola’s blog: RL, few-shot, variational inference
- Machine Learning Research Blog – Francis Bach: Focused on theoretical ML
- arg min blog: Focused on theoretical ML, a lot of experience
- Kiran Vodrahalli: Huge resources
- Machine Thoughts : Various topics, lots of opinions, AI general
- Off convex: ML theory
- Hunch: ML theory
- Cosma Rohilla Shalizi: CMU statistics prof, very good notebooks
- Windows on theory: Original thought on many topics
- Karl Stratos
- Machine Learning Blog | ML@CMU
- FastML
- OpenAI Blog
- Pinterest Engineering Blog – Medium
Math ML
Courses
- CMU 10606 - Math ML
- Theoretical Toolkit for CS
- Berkeley EE227BT - Convex Optimization
- Berkeley EE227C - Convex optimization and approximation
- Berkeley EECS 127/227 AT - Optimization models and applications
- Cornell CS 4783/5783 Mathematical foundations of machine learning
- Princeton ORF523 - Convex and Conic optimization
- Princeton ELE522 - Large-Scale Optimization for Data Science
- Cornell ORIE 6300 - Mathematical Programming I
- CMU 15-859(E) - Linear and Semidefinite Programming (Advanced Algorithms)
- CMU MATH 720 - Measure Theory and Integration
- NYU Mathematical tools for data science
- TTIC 31150/CMSC 31150 - Mathematical Toolkit
- CMPUT 340 - Introduction to Numerical Methods
Books/Survey
- Algebra, Topology, Differential Calculus, and Optimization Theory For Computer Science and Machine Learning by Jean Gallier and Jocelyn Quaintance
- Mathematics for Machine Learning -UC-Berkeley by Garrett Thomas
- Real Not Complex
- Real and Complex analysis by Walter Rudin
- Real Analysis: Measure Theory, Integration and Hilbert spaces by Elias M. Stein and Rami Shakarchi
- Matrix Cookbook by Kaare Brandt Petersen and Michael Syskind Pedersen
- Linear algebra review by Zico Kolter
- Probability and Measure - Course notes for STAT 571
- Convex Optimization: Algorithms and Complexity by Sébastien Bubeck
- Introductory Lectures on Convex Optimization: A Basic Course by Yurii Nesterov
- Convex Optimization by Stephen Boyd, Lieven Vandenberghe
- All the math you missed (but need to know for graduate school) by Thomas Garrity
- Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong
- Group theory by Mark McConnell
- Topology by James Munkres
- Proximal algorithms by Neal Parikh, Stephen Boyd
Papers
- Matrix Calculus You need for DL by Terence Parr and Jeremy Howard
- The math of AI by Gitta Kutyniok
- The modern math of DL by Julius Berner, Philipp Grohs, Gitta Kutyniok, Philipp Petersen
Computational ML
Courses
- CMU 10607 - Computational ML
- Numerics of Machine Learning
Books/Survey
Papers
Deep Learning
Courses
- TTIC 31230 - Fundamentals of DL
- CMU 10417 - Intermediate Deep Learning
- CMU 10414 - Deep Learning System
- CMU 10707 - Deep Learning
- Illinois - DL theory
- Princeton CS597B - Theoretical DL
- Uni Maryland CMSC 828W - Foundations of DL
- Stanford STATS 385 - Analyses of DL
- CS W182/282 - Designing, visualizing, and understanding deep neural networks
- MIT 65930 - Hardware architecture for DL
- Stanford STATS385 - Analyses of Deep Learning
- UC Berkeley Stat212b - Topics Course on Deep Learning
- MIT 18.177 - Mathematical Aspects of Deep Learning
- MIT 6.883 - Science of Deep Learning: Bridging Theory and Practice
Books/Survey
- Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville
- Neural network and Deep Learning by Michael Nielsen
- The Little Book of Deep Learning by Francois Fleuret
- Deep learning theory lecture notes by Matus Telgarsky
- Challenges in DL by Razvan Pascanu
- The principles of DL Theory by Daniel A. Roberts and Sho Yaida
- Theory of deep learning by Raman Arora, Sanjeev arora, Joan Bruna, Nadav Cohen, Rong Ge, Suriya Gunasekar, Chi Jin, Jason Lee, Tengyu Ma, Behnam Neyshabur, Zhao Song
- Deep learning: a statistical viewpoint by Peter Bartlett, Andrea Montanari, Alexander Rakhlin
- Mathematical introduction to deep learning: methods, implementations, and theory by Arnulf Jentzen, Benno Kuckuck, Philippe von Wurstemberger
Papers
- Open Problems in Applied Deep Learning by Maziar Raissi
- A statistician teaches deep learning by G. Jogesh Babu, David Banks, Hyunsoon Cho, David Han, Hailin Sang, Shouyi Wang
- Toward Theoretical Understanding of DL by Sanjeev Arora
- Recent advances in deep learning theory by Fengxiang He, Dacheng Tao
Machine Learning
Courses
- CMU 10716 - Advanced Machine Learning
- Cornell CS6780 - Advanced Machine Learning
- Caltech CS159 - Advanced ML
- Stanford STATS214/CS229M - Machine Learning Theory
- Cornell CS6783 - Machine Learning Theory
- Princeton CS 511 - Theoretical ML
- UofA CMPUT 654 - Theoretical foundations of ML
- TTIC 31250 - An Introduction to the Theory of Machine Learning
- CMUT 10715 - Advanced Intro to ML
- CMU 10605 - ML with large datasets
- CMU 10418 - ML for structured data
- Cornell ECE 5545 - ML hardware and systems
- Stanford CS 229s - System for ML
- Gaussian Process Summer School
- Berkeley EECS 208 - Computational Principles for High-Dimensional Data Analysis
- CMU 36708 - Statistical methods for machine learning
- Washington STAT 928 - Statistical Learning theory
- TTIC 31120: Computational and Statistical Learning Theory
- MIT 9.520/6.860 - Statistical Learning Theory and Applications
- CMU 36708 - The ABCDE of Statistical methods for ML
- TTIC 31120: Computational and Statistical Learning Theory
- Columbia COMS 4252 - Intro to computational learning theory
- CMU 36465/665 - Conceptual Foundations of Statistical Learning
- Caltech CS/CNS/EE/IDS 165 - Foundations of Machine Learning and Statistical Inference
- CMU 15859 - Algorithms for Big Data
- A Hands-on Approach for Implementing Stochastic Optimization Algorithms from Scratch
- UPenn - The algorithmic foundations of adaptive data analysis
- MIT 18408 - Algorithmic Aspects of ML
- Cornell 6781 - Foundations of Modern Machine Learning
- Columbia COMS 4772 - ML Theory
- Columbia COMS 4774 - Unsupervised Learning
- Princeton COS 598 - Unsupervised Learning: Theory and Practice
- A course in ML by Hal Daume
- Duke COMPSCI 590.2 - Algorithimic Aspects of Machine Learning
- Princeton CS 597A - New Directions in Theoretical Machine Learning
- Simons - Foundations of Machine Learning
- Simons - Foundations of Data Science
- Machine Learning with kernel methods
- Stanford CS364A - Algorithmic Game Theory
- Michigan EECS 598 - Theoretical foundations of ML
Books/Survey
- Pen and Paper Exercises in ML by Michael U. Gutmann
- Patterns, Predictions, and Actions - A story about machine learning by Moritz Hardt and Benjamin Recht
- Mathematical Analysis of Machine Learning Algorithms by Tong Zhang
- Artificial Intelligence: A modern approach by Stuart Russell and Peter Norvig
- Deep Learning Cheatsheet by Afshine Amidi and Shervine Amidi
- Recent Advances in Bayesian Optimization
- The Algorithmic Foundations of Differential Privacy by Cynthia Dwork, Aaron Roth
- Foundations of Data science by Avrim Blum, John Hopcroft, and Ravindran Kannan
- Provable Algorithms for machine learning problems by Rong Re
- CS229T/STAT231: Statistical Learning Theory (Winter 2016) by Percy Lianghttps://web.stanford.edu/class/cs229t/2017/Lectures/percy-notes.pdf
- Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David
- Foundations of Machine Learning by Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar
- Learning with Kernels by Bernhard Schölkopf and Alexander J. Smola
- Linear Dimensionality Reduction: Survey, Insights, and Generalizations by John P. Cunningham, Zoubin Ghahramani
- Computational optimal transport by Gabriel Peyré, Marco Cuturi
- Pattern Recognition and Machine Learning by Christopher M. Bishop
- Machine Learning: A Probabilistic Perspective by Kevin Murphy
- Probabilistic Machine Learning: An Introduction by Kevin Murphy
- Probabilistic Machine Learning: Advanced Topics by Kevin Murphy
- Sampling algorithms
Papers
- Theory of classification: A Survey of some recent advances by Stephane Boucheron, Olivier Bousquet, and Gabor Lugosi
- Bayesian nonparametrics and the probabilistic approach to modelling by Zoubin Ghahramani
- An introduction to Hidden Markov Models and Bayesian networks by Zoubin Ghahramani
- A Unifying Review of Linear Gaussian Models by Sam Roweis, Zoubin Ghahramani
- Bayesian Nonparametric Models by Peter Orbanz, Yee Whye Teh
- Dirichlet Process by Yee Whye Teh
- Hierarchical Bayesian Nonparametric Models with Applications by Yee Whye Teh
Reinforcement Learning
Courses
- CMU 10703 - Deep RL
- Berkeley CS285 - Deep RL
- Stanford CS224R - Deep RL
- CMPUT 655 - RL 1 - Graduate
- CMPUT 605- RL Theory Grad
- Washington CSE 599 - RL and Bandits
- MIT 67950 - RL Foundations and Methods
- Cornell CS 6789 - Foundations of RL
- Illinois CS 542 - Statistical RL
- Princeton COS 597R - Probabilistic Topics in RL
- McGill ECSE 506 - Stochastic control and decision theory
- Stanford EE263: Introduction to Linear Dynamical Systems
- Columbia Dynamic Programming and RL
- David Silver RL course
- Purdue CS58300: RL
- Columbia Dynamic programming and reinforcement learning
Books/Survey
- A succint Summary of Reinforcement Learning by Sanjeevan Ahilan
- An Introduction to Deep RL Vincent François-Lavet, Peter Henderson, Riashat Islam, Marc G. Bellemare and Joelle Pineau
- Empirical Design in Reinforcement Learning
- Towards Continual RL: A review and perspectives by Khimya Khetarpal, Matthew Rieme, Irina Rish, Doina Precup
- A survey of Meta-RL by Jacob Beck, Risto Vuorio, Evan Zheran Liu, Zheng Xiong, Luisa Zintgraf, Chelsea Finn, Shimon Whiteson
- Reinforcement Learning: An Introduction by Richard S. Sutton, Andrew G. Barto
- Reinforcement Learning: Theory and Algorithms by Alekh Agarwal, Nan Jiang, Sham M. Kakade, Wen Sun
- Algorithms for Reinforcement Learning by Csaba Szepesvari
- Dynamic Programming and Optimal Control by Dimitri P. Bertsekas
- Simulation and the Monte Carlo Method by Reuven Y. Rubinstein, Dirk P. Kroese
- Practical Methods for Optimal Control and Estimation using nonlinear programming by John T. Betts
- Multi-agent Reinforcement Learning: Foundations and Modern Approaches by Stefano V. Albrecht, Filippos Christianos, Lukas Schäfer
- RL, bit by bit by Xiuyuan Lu, Benjamin Van Roy, Vikranth Dwaracherla, Morteza Ibrahimi, Ian Osband and Zheng Wen
- A Tour of Reinforcement Learning: The View from Continuous Control by Benjamin Recht
Papers
- Mathematical Foundations of Monte Carlo Methods
Online Learning
Courses
- Southern Cali CSCI 659 - Intro to Online Optimization/Learning
- Berkeley EE290/CS194 - ML for sequential decision making under uncertainty
- Victoria CSC 482/581 - Intro to Online Learning
- Washington CSE599 - Online Learning
- Berkeley EE 290 - Theory of Multi-armed Bandits and RL
- Washington CSE 599M - Interactive Machine Learning in Non-stochastic Environments
- Illinois IE 498: Online Learning and Decision Making
- Columbia COMS E6998.001: Bandits and RL
- MIT 6883: Online methods in ML
- Remark: These courses are closely related to Reinforcement Learning.
Books/Survey
- Introduction to Online Optimization by Sebastien Bubeck
- A modern Introduction to Online Learning by Francesco Orabona
- Introduction to Online Convex Optimization by Elad Hazan
- Online Learning and Online Convex Optimization by Shai Shalev-Shwartz
- Online Learning: A comprehensive Survey by Steven C.H. Hoi, Doyen Sahoo, Jing Lu, Peilin Zhao
- Bandit algorithms by Tor Lattimore and Csaba Szepesvari
- Regret Analysis of Stochastic and Nonstochastic multi-armed bandit problem by Sebastien Bubeck and Nicolo Cesa-Bianchi
- Introduction to Multi-armed bandits by Aleksandrs Slivkins
- A tutorial on Thompson Sampling Daniel J. Russo , Benjamin Van Roy , Abbas Kazerouni, Ian Osband and Zheng Wen
- Online Evaluation for Information Retrieval by Katja Hofmann, Lihong Li, Filip Radlinski
- Prediction, Learning, Games by Nicolo Cesa-Bianchi, Gabor Lugosi
- Bandit lecture notes by Kevin Jamieson
- From Bandits to Monte-Carlo Tree Search: The optimistic principle applied to optimization and planning by Remi Munos
Papers
Geometric DL/ Graph NN
Courses
- AMMI Geometric DL
- Stanford CS224W - Machine Learning with Graphs
- UPenn - GNN
- Stanford CS246: Mining Massive Data Sets
Books/Survey
- Geometric Deep Learning Grids, Groups, Graphs, Geodesics, and Gauges by Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar Veličković
Papers
Meta Learning
Courses
- Stanford CS 330 - Deep Multi-task and meta learning
Books/Survey
Papers
Representation Learning
Courses
- Mila IFT 6135 Representation Learning
Books/Survey
Papers
PGM
Courses
- Stanford CS228 - PGM
- UofT CSC 412 - Probabilistic ML
- CMU 10708 - PGM
Books/Survey
- [Probabilistic graphical model: Principles and techniques by Daphne Koller and Nir Friedman]
Papers
Statistics
Courses
- UofT STA314H1F - Statistical Learning theory I
- Uoft STA414 - Statistical Learning theory II
- Berkeley EECS 126 - Probability and Random processes
- Berkeley Stat210B - Theoretical Statistics
- CMU 36705 - Intermediate Statistics
- Stanford STATS 300B: Theory of Statistics II
- Statistics 311/Electrical Engineering 377: Information Theory and Statistics
- Stanford EE 378B – Inference, Estimation, and Information Processing
- Harvard CS 229r - Information Theory in Computer Science
- Harvard CS 229r - Essential Coding Theory
- MIT 18657 - High dimensional probability
- Princeton MAT 589 - Topics in Probability, Statistics and Dynamics: Modern discrete probability theory
Books/Survey
- Probability Theory Survey by Arian Maleki and Tom Do
- Statistics Proof book
- Probability with Martingales by David Williams
- High-dimensional statistics: A non-asymptotic viewpoint by Martin J. Wainwright
- High-dimensional data analysis with low-dimensional models: principles, computation, and applications by John Wright and Yi Ma
- High-dimensional Probability: An introduction with Applications in Data Science by Roman Vershynin
- Probability in High dimension by Ramon van Handel (Princeton)
- Introduction to Statistical Learning Theory Olivier Bousquet, Stephane Boucheron, and Gabor Lugosi
- Illinois ECE 543 Statistical Learning Theory by Bruce Hajek and Maxim Raginsky
- Oxford Modern Statistical Theory by George Deligiannidis
- All of statistics: A concise Course in statistical inference by Larry Wasserman
- All of nonparametric statistics by Larry Wasserman
- Introduction to Nonparametric Estimation by Alexandre B. Tsybakov
- Mathematical Statistics by Jun Shao
- Asymptotic Statistics by A. W. van der Vaart
- A Survey on Distribution Testing: Your Data is Big. But is it Blue? by C. Canonne
- Introduction to the non-asymptotic analysis of random matrices by Roman Vershynin
- Concentration inequalities: A nonasymptotic theory of independence by Stephane Boucheron, Gabor Lugosi, Pascal Massart
- An Introduction to Matrix Concentration Inequalities by Joel A. Tropp
- Modern Discrete Probability: An Essential Toolkit by Sebastien Roch
- MATH 170A - Probability theory by Steven Heilman
- MATH 170B - Probability theory by Steven Heilman
- The elements of statistical learning: Data mining, inference and prediction - Trevor Hastie, Robert Tibshirani, Jerome Friedman
- A first course in probability by Sheldon Ross
- A second course in probability by Sheldon Ross
- Mathematical Statistics with applications by Wackerly, Mendenhall, Scheaffer
- Introduction to mathematical statistics by Hogg, McKean, Craig
- Elements of information theory by Thomas Cover, Joy Thomas
- Applied linear statistical models by Michael Kutner, Christopher Nachtsheim, John Neter, William Li
- Fundamentals of Statistical Exponential Families with Applications in Statistical Decision Theory by Lawrence D. Brown
Papers
NLP
Courses
- Stanford CS224d - Deep Learning for Natural Language Processing
- Harvard CS287 - Machine Learning for Natural Language
- CMU CS 11-747 - Neural Networks for NLP
- CMU CS 11-731 - Machine Translation and Sequence-to-sequence Models
- Princeton COS495 Natural Language Processing
Surveys/Books
- A Primer on Neural Network Models for Natural Language Processing by Yoav Goldberg
Papers
Foundations
Courses
- CMU 15451 - Algorithms
- CMU 15213 - Introduction to computer systems
- CMU 15-213/15-513/14-513 - Introduction to Computer Systems
- Stanford CS 111 - OS
- Harvard CS 125: Algorithms and Complexity
- Columbia COMS 6998-006 - Foundations of Blockchains
- Columbia COMS 4995 - Randomized Algorithms
Books/Survey
- Notes on Randomized Algorithms by James Aspnes
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Papers