There's a culture in ML of authors making their textbooks available online (to supplement the traditional print editions), which is extremely beneficial to students & researchers. The following is a list of machine learning textbooks that the authors have made freely available on their websites. It includes 2 of our course textbooks.

(Perhaps we could refactor this into a Wiki or Markdown document at some point so that others can add to it going forward.)

ML Theory

Foundations of Machine Learning Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar MIT Press, Second Edition, 2018. https://cs.nyu.edu/~mohri/mlbook/

Understanding Machine Learning: From Theory to Algorithms Shai Shalev-Shwartz and Shai Ben-David Cambridge University Press, 2014 http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/copy.html

A Probabilistic Theory of Pattern Recognition Authors: Devroye, Luc, Györfi, László, Lugosi, Gábor Springer 1996 www.szit.bme.hu/~gyorfi/pbook.pdf

High-Dimensional Probability: An Introduction with Applications in Data Science Roman Vershynin Cambridge University Press, 2018 https://www.math.uci.edu/~rvershyn/papers/HDP-book/HDP-book.html#

Information Theory, Inference, and Learning Algorithms David J.C. MacKay Cambridge University Press, 2003 http://www.inference.org.uk/itila/book.html

ML Methods

General

Pattern Recognition and Machine Learning Christopher Bishop Springer, 2006 https://www.microsoft.com/en-us/research/people/cmbishop/#!prml-book

The Elements of Statistical Learning: Data Mining, Inference, and Prediction Trevor Hastie, Robert Tibshirani, and Jerome Friedman Springer, Second Edition, 2009 http://web.stanford.edu/~hastie/ElemStatLearn/

Sparse

Statistical Learning with Sparsity: The Lasso and Generalizations Trevor Hastie, Robert Tibshirani, and Martin Wainwright CRC Press, 2016 https://web.stanford.edu/~hastie/StatLearnSparsity/

Probabilistic

Bayesian Reasoning and Machine Learning David Barber Cambridge University Press, 2012 http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage

Gaussian Processes for Machine Learning Carl Edward Rasmussen and Christopher K. I. Williams MIT Press, 2006 http://www.gaussianprocess.org/gpml/

Deep Learning, Reinforcement Learning & Neural Networks

Deep Learning Ian Goodfellow, Yoshua Bengio, and Aaron Courville MIT Press, 2016 http://www.deeplearningbook.org/

Reinforcement Learning: An Introduction Richard S. Sutton and Andrew G. Barto MIT Press, Second Edition, 2018 http://incompleteideas.net/book/the-book-2nd.html

Neural Networks and Deep Learning Michael Nielsen http://neuralnetworksanddeeplearning.com/

Dive into Deep Learning Aston Zhang, Zack C. Lipton, Mu Li, and Alex J. Smola http://d2l.ai/

Convex Optimization

Convex Optimization Stephen Boyd and Lieven Vandenberghe Cambridge University Press, 2004 http://stanford.edu/~boyd/cvxbook/

Convex Optimization: Algorithms and Complexity Sébastien Bubeck NOW, 2015 http://sbubeck.com/Bubeck15.pdf

Miscellaneous

Mathematical Foundations of Data Sciences Gabriel Peyré (Draft, 2019) https://mathematical-tours.github.io/book/

tevgeniou / FoundationsML

ML textbooks that are available electronically #4