Books

Dudley. 2002. Real Analysis and Probability.

Murphy. 2022. Probabilistic Machine Learning.

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

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

Bishop. 2006. Pattern Recognition and Machine Learning.

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

https://d2l.ai

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

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

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

Vershynin. 2018. High-Dimensional Probability.

Mathematical Foundations of Infinite-Dimensional Statistical Models

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

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

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. 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.

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

~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: https://mlstory.org/

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

https://press.princeton.edu/books/hardcover/9780691198309/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.

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

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.

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

Deep Learning For Distributed Big Data Analytics.

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

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

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

Stroup W., 2012. Generalized Linear Mixed Models.

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

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.