| Of the top of my head, - Machine Learning by Tom M Mitchell
http://www.cs.cmu.edu/~tom/mlbook.html For general reading and introductions I also like: - Pattern Classification by Richard Duda - Pattern Recognition and Machine Learning by
Christopher Bishop For a bit more emphasis on statistics and math, I usually dive in to - Classification,Parameter Estimation and State Estimation by van der Heijden And last, but certainly not least: - Information Theory, Inference, and Learning Algorithms by
David MacKay, available here: http://www.inference.phy.cam.ac.uk/mackay/itila/ |
I've read O'Reilly's Collective Intelligence. It's a great introductory survey, but it was very light on theory.
I also own Collective Intelligence in Action. It had more explanation of theory than O'Reilly's offering, but most of the chapters devolved into how to use Java data mining framework X.