The Online Books Page

Supervised learning (Machine learning)

See also what's at Wikipedia, your library, or elsewhere.

• Machine learning

• Boosting (Algorithms)

• Learning, Supervised (Machine learning)

• Boosting: Foundations and Algorithms (Cambridge, MA and London: MIT Press, c2012), by Robert E. Schapire and Yoav Freund (HTML with commentary at MIT Press)

• Boosting: Foundations and Algorithms (Cambridge, MA and London: MIT Press, c2012), by Robert E. Schapire and Yoav Freund (HTML with commentary at MIT Press)

Items below (if any) are from related and broader terms.

• Graph Representation Learning (prepublication version, 2020), by William L. Hamilton (PDF at McGill)
• Probabilistic Machine Learning for Civil Engineers (prepublication version; 2020), by James-A. Goulet (PDF files with commentary at polymtl.ca)
• Foundations of Machine Learning (second edition; Cambridge, MA and London: MIT Press, c2018), by Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar (HTML at ublish.com)
• Elements of Causal Inference: Foundations and Learning Algorithms (Cambridge, MA and London: MIT Press, c2017), by Jonas Peters, Dominik Janzing, and Bernhard Schölkopf (PDF with commentary at MIT Press)
• Machine Learning (main text c1997; additional chapters c2017), by Tom M. Mitchell (PDF with commentary at CMU)
• Deep Learning (prepublication version; 2016), by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (illustrated HTML with commentary at deeplearningbook.org)
• Bayesian Reasoning and Machine Learning (free online edition, c2014), by David Barber (PDF with commentary in the UK)
• The Elements of Statistical Learning: Data Mining, Inference, and Prediction (second edition, 2009), by Trevor Hastie, Robert Tibshirani, and J. H. Friedman (PDF with commentary at Stanford)
• Reinforcement Learning: An Introduction (first and second editions, 1998 and 2018), by Richard S. Sutton and Andrew G. Barto (HTML and PDF at incompleteideas.net)
• Pattern Recognition and Machine Learning (c2006), by Christopher M. Bishop (PDF at microsoft.com)
• Ethics and Data Science (c2019), by Michael Kosta Loukides, Hilary Mason, and DJ Patil (HTML at oreilly.com)
• A Course in Machine Learning, by Hal Daumé (PDF with commentary at ciml.info)
• Machine Learning, Neural and Statistical Classification, ed. by Donald Michie, D. J. Spiegelhalter, and Charles C. Taylor (PDF and gzipped Postscript in the UK)

• Gaussian Processes fo Machine Learning (online edition; originally published Cambridge, MA and London: MIT Press, c2006), by Carl Edward Rasmussen and Christopher K. I. Williams (PDF with commentary at gaussianprocess.org)

• Mathematics for Machine Learning (prepublication free online edition; 2020), by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong (PDF with commentary at github.io)

• Fairness and Machine Learning: Limitations and Opportunities (prepublication version; 2019), by Solon Barocas, Moritz Hardt, and Arvind Narayanan (HTML with commentary at fairmlbook.org)

• Journal of Machine Learning Research (2000-) (full serial archives)

• Learning Deep Architectures for AI (c2009), by Yoshua Bengio (PDF at Wayback Machine)

• Reinforcement Learning: An Introduction (first and second editions, 1998 and 2018), by Richard S. Sutton and Andrew G. Barto (HTML and PDF at incompleteideas.net)