in alphabetical order by author:
- Alpaydin, Introduction to Machine Learning, MIT Press,
2004. [For more on this text, including Errata, see
http://www.cmpe.boun.edu.tr/~ethem/i2ml
- Bishop, Neural Networks for Pattern Recognition, Oxford University
Press, 1996.
- Cristianini and Shawe-Taylor, An Introduction to Support Vector Machines
and other kernel-based learning methods, Cambridge, 2000.
- Duda, Hart, and Stork, Pattern Classification, 2nd Edition, Wiley,
2001. [For more on this text, including Errata, see
http://rii.ricoh.com/~stork/DHS.html
- Hand, Mannila, and Smyth, Principles of Data Mining, MIT Press,
2001.
- Hastie, Tibshirani, Friedman, The Elements of Statistical Learning:
Data Mining, Inference, and Prediction, Springer, 2001.
- Kearns and Vazirani, Computational Learning Theory, MIT Press,
1994.
- Michie, Spiegelhalter, Taylor, Machine Learning, Neural and
Statistical Classification,
(download).
- Mitchell, Machine Learning, McGraw Hill, 1997.
- Nilsson, Introduction to Machine Learning,
(download).
- Russell and Norvig, Artificial Intelligence: A Modern Approach,
Prentice Hall, 2003.
- Shawe-Taylor and Cristianini, Kernel Methods for Pattern Analysis,
Cambridge, 2004.
- Sutton and Barto, Reinforcement Learning: An Introduction,
(download).
- Witten and Frank, Data Mining: Practical Machine Learning Tools and
Techniques, 2nd Edition, Morgan Kaufmann, 2005.