The ML/AI field is huge. It involves way too many fields and subfields. Since any number, whether directly recorded or derived from physical observations, or even psychological perceptions, can be considered “data”, hence there are simply too many subjects to be tagged “data science”.
Below I will begin compiling a list of books (though some may simply be manuscripts from professors) that are well known, read, and/or cited for Ph.D. students to grip the noteworthy theories and practices. I will update this list frequently so please feel free to come back often.
Pattern Recognition and Machine Learning. Christopher Bishop.
Machine Learning: A Probabilistic Perspective. Kevin P. Murphy.
Deep Learning. Ian Goodfellow, Yoshua Bengio, Aaron Courville.
Computer Age Statistical Inference: Algorithms, Evidence and Data Science. Bradley Efron, Trevor Hastie.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Trevor Hastie, Robert Tibshirani, Jerome Friedman.
Advanced Data Analysis from an Elementary Point of View. Cosma Rohilla Shalizi.
Subject-focused textbooks / manuscripts:
Graphical Models, Exponential Families, and Variational Inference. Martin J. Wainwright, Michael I. Jordan.
An Introduction to Conditional Random Fields. Charles Sutton, Andrew McCallum.
Categorical Data Analysis. Alan Agresti.
Introductory Lectures on Convex Optimization. Yurii Nesterov.
Convex Optimization. Stephen Boyd, Lieven Vandenberghe.
Probability Theory / Measure Theory
Introduction to Probability Models. Sheldon M. Ross.
Measure Theory and Fine Properties of Functions. Lawrence Craig Evans, Ronald F. Gariepy
Probability Essentials. Jean Jacod, Philip Protter.
Probabilistic Symmetries and Invariance Principles. Olav Kallenberg.
Stochastic Process / Stochastic Differential Equations
Poisson Processes. J. F. C. Kingman.
Stochastic Methods. Crispin Gardiner.
An Introduction to Stochastic Differential Equations. Lawrence Craig Evans.
Determinantal point processes for machine learning. Alex Kulesza, Ben Taskar.
Gaussian Processes for Machine Learning. Carl Edward Rasmussen, Christopher K. I. Williams.
Introductory Functional Analysis with Applications. Erwin Kreyszig.
Ordinary / Partial Differential Equations
Partial Differential Equations. Lawrence Craig Evans.
Statistical Inference (classical)
Statistical Inference. George Casella, Roger L. Berger.
Testing Statistical Hypotheses. Erich L. Lehmann, Joseph P. Romano.
Bayesian Data Analysis. Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin.
Bayesian Approximate Inference
Handbook of Markov Chain Monte Carlo. Steve Brooks, Andrew Gelman, Galin L. Jones, Xiao-Li Meng.
Reinforcement Learning: An Introduction. Richard S. Sutton, Andrew G. Barto.
Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Guido W. Imbens, Donald B. Rubin.
Causality: Models, Reasoning and Inference. Judea Pearl.
Counterfactuals and Causal Inference: Methods and Principles for Social Research. Christopher Morgan, Stephen Winship.
Causal Inference. Miguel A. Hernán, James M. Robins.
Elements of Causal Inference: Foundations and Learning Algorithms. Jonas Peters, Dominik Janzing, Bernhard Schölkopf.
Information Retrieval. Christopher Manning, Prabhakar Raghavan,
Mostly Harmless Econometrics: An Empiricist’s Companion. Joshua D. Angrist, Jörn-Steffen Pischke.
Mathematical & Computational Finance
Quantum Physics / Chemistry
Algebraic & Computational Game Theory