Machine Learning Textbooks: Excellent and Online

This is a list of textbooks about machine learning which are (a) really good, and (b) free on the web.

• The Elements of Statistical Learning: Data Mining, Inference, and Prediction, by Trevor Hastie, Robert Tibshirani, and Jerome Friedman (deep coverage of an important subset of the field)
• Information Theory, Inference, and Learning Algorithms, by David MacKay (very mathematical approach, excellent but hard core)
• Reinforcement Learning: An Introduction, by Richard S. Sutton and Andrew G. Barto (dated, but the bible of reinforcement learning)
• Bayesian Reasoning and Machine Learning, by David Barber (incomplete coverage of the field, but solid and accessible and includes Octave/Matlab code)
• Machine Learning by Simon Rogers (I will be using some of the chapters as lecture notes)
• Introduction to Machine Learning: Draft of Incomplete Notes by Nils J. Nilsson (excellent writer; his earlier book on AI saved my bacon; slightly dated)

If you know of others (criteria: of general Machine Learning interest, not something highly specific like Gaussian Processes) post a comment and I'll add above.

(Updated 13-Oct-2010)

Digits Dataset

The MNIST dataset of labelled handwritten digits: http://yann.lecun.com/exdb/mnist/.  (Thanks to Yann LeCun and Corrina Cortes for cleaning the dataset and making it publicly available.)

R

Install and play with (i.e., learn) R: http://www.r-project.org

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