https://probml.github.io/pml-book/book1.html

Massive. A lot of topics covered. Tons of references. Weird-shit probabilistic notation.

https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf 758 pages. Classic. Many exercises.

PAC framework. VC theory. Mehryar Mohri – Foundations of Machine Learning - Book

https://d2l.ai

High-Dimensional Data Analysis with Low-Dimensional Models | Higher Education from Cambridge

https://book-wright-ma.github.io/

Mathematical Foundations of Infinite-Dimensional Statistical Models

Juicy math. Non-parametric statistics. http://www.statslab.cam.ac.uk/~nickl/Site/__files/FULLPDF.pdf

https://www.cambridge.org/core/books/principles-of-deep-learning-theory/3E566F65026D6896DC814A8C31EF3B4C#fndtn-information

[2106.10165] The Principles of Deep Learning Theory

Computer Age Statistical Inference, Student Edition | Higher Education from Cambridge

~500 pages. Math notation. No proofs? Emphasis on connection of classical statistics and how methods and math evolved over time. GLMs. Poisson regression. Jackknife and Bootstrap.

https://www.cambridge.org/core/books/machine-learning-fundamentals/76F6FE4B396A7CF82EFD23FD1FBF4DA8

~400 pages. Nice math notation.

Inline python code. Good math notation. Some proofs. Many exercises. ~500 pages.

~414 pages. Math notation ok. No proofs. EM algorithm. Gaussian processes. Gibbs sampling. Mixture models.

Patterns, Predictions, and Actions | Princeton University Press

Available for download: https://mlstory.org/

https://press.princeton.edu/books/hardcover/9780691198309/statistics-data-mining-and-machine-learning-in-astronomy

Inline python code.

Machine Learning Refined | Higher Education from Cambridge

A lot of blabla.

https://www.routledge.com/Transformers-for-Machine-Learning-A-Deep-Dive/Kamath-Graham-Emara/p/book/9780367767341

Artificial Intelligence and Causal Inference - 1st Edition - Momiao X

Connections to DL and RL.

https://www.cambridge.org/core/books/deep-learning-on-graphs/CF908050EECC148A9E6F3EAED6099DB4

https://www.cambridge.org/core/books/edge-learning-for-distributed-big-data-analytics/37D8FC64C94641362BE9AFCDBCD0AD9B

https://www.routledge.com/Introduction-to-General-and-Generalized-Linear-Models/Madsen-Thyregod/p/book/9781420091557

https://www.routledge.com/Generalized-Linear-Mixed-Models-Modern-Concepts-Methods-and-Applications/Stroup/p/book/9781439815120

Math tool shallow.

Introduction to Machine Learning, Fourth Edition | The MIT Press

Many topics covered, but shallow, and not very mathematical.