Study Notes

Learning Materials

• Modern Statistics

  • Foundations:

    1. Probability and Measure with videos by Adam B Kashlak

    2. High-dimensional Probability with video by Roman Vershynin

    3. High-dimensional Statistics by Martin J. Wainwright

    4. Asymptotics Statistics by Aad W Van der Vaart

    5. A First Look at Stochastic Processes by Jeffrey S. Rosenthal

    6. Mathematical Statistics by Jun Shao

  • Advanced:

    1. Robust and Non-parametric Statistics (course 1 and course 2) 

    2. Empirical Process: introduction, applications to semi-parametric model, and  Weak Convergence and Empirical Processes 

    3. Causal Inference:

       • • A First Course in Causal Inference by Peng Ding 

         • Causal Inference: A Statistical Learning Approach by Stefan Wager

         • Applied Causal Inference from Nathan Kallus

    4. Experiment Design (lectures) 

    5. Testing Statistical Hypothesis  

    6. Nonlinear Time Series by Jianqing Fan and Qiwei Yao 

    7. Information Theory  

    8. Optimal Transport course

    9. Bayesian Optimization by Roman Garnett

• Machine Learning:

  • ML Theory: ML Theory Lecture from Csaba Szepesvári 

  • Statistical ML: 

    1. Statistical Learning Methods (Chinese) by Hang Li 

    2. The Elements of Statistical Learning (ESL) by Trevor Hastie, Robert Tibshirani and Jerome Friedman 

    3. Statistical ML (course 1 & course 2 by Ryan Tibshirani and Larry Wasserman, course 3 by Danica Sutherland)  

  • Probabilistic ML: Probabilistic Machine Learning: Advanced Topics by Kevin Patrick Murphy

  • Classical Textbooks:  (1) Understanding Machine Learning: From Theory to Algorithms. (2) Foundations of Machine Learning (3) Computer Age Statistical Inference.

• Deep Learning: 

  • Introduction:  Deep learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville

  • Theory: Mathematical Analysis of Machine Learning Algorithms by Tong Zhang

• Reinforcement Learning:

  • Introduction:  

    1.  Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto [the first book to learn RL]

    2.  Introduction to RL (Coursera) by Matha White and Adam White 

    3. Deep RL courses by Sergey Levine

    4. Reinforcement Learning by Emma Brunskill (Stanford CS234)

    5. Introduction to RL by Lucas Janson (Harvard)

    6. Reinforcement Learning: An Overview by Kevin M. Murphy [modern progress in different directions of RL]

  • Theoretical RL:  

    1. CMPUT 653 RL Theory by  Csaba Szepesvári 

    2. Theory of RL by Ambuj Tewari

    3. Foundations of RL (PKU Mooc) by Zhihua Zhang

  • Control Theory: 

    1. Stochastic Control and Decision Theory (McGill)

  • Statistical RL:  

    1. Statistical RL courses by Nan Jiang 

    2. Reinforcement Learning: Theory and Algorithms by Alekh Agarwal Nan Jiang Sham M. Kakade Wen Sun

• Large Language Models

  • Reinforcement Learning of Large Language Models, by Ernest K. Ryu (UCLA)

  • Transformers United, Stanford CS25