Dudley. 2002. Real Analysis and Probability.

Murphy. 2022. Probabilistic Machine Learning.

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

Bishop. 2006. Pattern Recognition and Machine Learning. 758 pages. Classic. Many exercises.

Mohri. 2018. Foundations of Machine Learning. 2nd ed.

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

Dive into Deep Learning. Online book.

Wright, Ma. 2022. High-Dimensional Data Analysis with Low-Dimensional Models.

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

Vershynin. 2018. High-Dimensional Probability.

Mathematical Foundations of Infinite-Dimensional Statistical Models

Juicy math. Non-parametric statistics.

Roberts, Yaida, 2022. The Principles of Deep Learning Theory.

[2106.10165] The Principles of Deep Learning Theory

Computer Age Statistical Inference. Efron, Hastie. 2021.

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.

Hui Jiang. 2022. Machine Learning Fundamentals. A Concise Introduction.

~400 pages. Nice math notation.

Kroese, Botev, Taimre, Vaisman. 2022. Data Science and Machine Learning.

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

Rogers, Girolani. 2017. A First Course in Machine Learning.

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

Hardt and Recht. 2022 Oct. Patterns, Predictions, and Actions: Foundations of Machine Learning.

Patterns, Predictions, and Actions | Princeton University Press

Available for download:

Ivezić, Connolly, VanderPlas, Gray. 2020. Statistics, Data Mining, and Machine Learning in Astronomy.

Inline python code.

Edge Learning for Distributed Big Data Analytics.

Watt, Borhani, Katsaggelos. 2020. Machine Learning Refined. Foundations, Algorithms, and Applications.

Machine Learning Refined | Higher Education from Cambridge

A lot of blabla.

Kamath, Graham, Emara. 2022. Transformers for Machine Learning.

Momiao Xiong, 2022. Artificial Intelligence and Causal Inference.

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

Connections to DL and RL.

Deep Learning on Graphs. 2021.

Deep Learning For Distributed Big Data Analytics.

Madsen, Thyregod. 2010. Introduction to General and Generalized Linear Models.

Stroup W., 2012. Generalized Linear Mixed Models.

Faul. A concise introduction to machine learning.

Math tool shallow.

Alpaydin. Introduction to Machine Learning.

Introduction to Machine Learning, Fourth Edition | The MIT Press

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