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.
Introductory / Beginners level (ML overview):
Pattern Recognition and Machine Learning. Christopher Bishop.
Machine Learning: A Probabilistic Perspective. Kevin P. Murphy.
Deep Learning. Ian goodfellow, Yoshua Bengio, Aaron Courville.
Intermediate & Advanced (By topic)
Graphical Models, Exponential Families, and Variational Inference. Martin J. Wainwright, Michael I. Jordan.
Introductory Lectures on Convex Optimization. Yurii Nesterov.
Partial Differential Equations
Statistical Inference (classical)
Bayesian Approximate Inference
Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Guido Imbens, Donald Rubin.
Causality: Models, Reasoning and Inference. Judea Pearl.
Counterfactuals and Causal Inference: Methods and Principles for Social Research. Christopher Mordan, Stephen Winship.
Information Retrieval. Christopher Manning, Prabhakar Raghavan,
Mostly Harmless Econometrics: An Empiricist’s Companion. Joshua D. Angrist, Jörn-Steffen Pischke.
Quantum Physics / Chemistry
Algebraic Game Theory