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/
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/