Probability & Statistics

Probability Theory

1. Wasserman chapters 1-5
2. Probability Course

Statistical Learning Theory

1. ESL chapter 1, 5
2. MacKay chapter 3
3. Wasserman chapter 6, 8-10

Supervised Learning

1. ESL chapter 2

Regression

1. ESL chapter 3
2. Wasserman chapter 13

Classification

1. ESL chapter 4
2. MacKay chapter 20-22

Model Selection and Complexity

1. ESL chapter 7
2. MacKay chapter 28

Additive Models and Trees

1. ESL chapters 9-10

Information and Coding Theory

1. MacKay chapters 4-6, 8-17, 24

Probabilistic Graphical Models

Graphical Models

1. Murphy chapter 4

Undirected Models

1. MacKay chapter 31
2. Wasserman chapter 18

Directed Models

1. MacKay chapter 37
2. Wasserman chapter 17

Variational Methods

1. MacKay chapter 33

Sampling and Monte Carlo Methods

1. MacKay chapters 29-30, 32
2. Bishop-Bishop chapter 14

Deep Generative Models

1. MacKay chapter 43
2. VAEs
3. Bishop-Bishop chapters 17-18, 20

Causality and GNNs

1. Bishop-Bishop chapter 13
2. Wasserman chapter 16
3. Bayesian Epistemology
4. Elements of Causal Inference chapters 1, 3-4
5. Pearl chapters 7-11

Bibliography

• Wasserman: Larry Wasserman; All of Statistics
• Probability Course: H. Pishro-Nik; Introduction to probability, statistics, and random processes
• ESL: Trevor Hastie, Robert Tibshirani and Jerome Friedman; Elements of Statistical Learning
• MacKay: David J. C. MacKay; Information Theory, Inference, and Learning Algorithms
• PGM: Daphne Koller and Nir Friedman; Probabilistic Graphical Models: Principles and Techniques
• Bishop: Christopher M. Bishop; Pattern Recognition
• Bishop-Bishop: Christopher M. Bishop and Hugh Bishop; Deep Learning: Foundations and Concepts
• Murphy: Kevin Murphy; Machine Learning: Advanced Topics
• Blei et al.: David M. Blei, Alp Kucukelbir, Jon D. McAuliffe; Variational Inference: A Review for Statisticians
• VAEs: Diederik P. Kingma, Max Welling; An Introduction to Variational Autoencoders
• Bayesian Epistemology: https://plato.stanford.edu/entries/epistemology-bayesian/
• Pearl Judea Pearl; Causality
• Elements of Causal Inference: Peters, Janzing and Scholkopf; Elements of Causal Inference
• Wainwright-Jordan: Martin J. Wainwright and Michael I. Jordan; Graphical Models, Exponential Families, and Variational Inference