I am looking for book ideas to refresh my machine learning knowledge. I have some but I wanted to see what the top universities are using these days.

Here are a couple of classes (Stanford has online courses):

Carnegie Mellon’s class:

• Textbook: Pattern Recognition and Machine Learning , Chris Bishop.
• Secondary textbook: The Elements of Statistical Learning: Data Mining, Inference, and Prediction Trevor Hastie, Robert Tibshirani, Jerome Friedman. 2nd edition.
• Optional textbook: Machine Learning , Tom Mitchell.
• Optional textbook: Information Theory, Inference, and Learning Algorithms , David Mackay.

MIT’s Class:

• Cowell et al., "Probabilistic networks and expert systems", Springer-Verlag, 1999.
• Bishop, "Neural Networks for Pattern Recognition", 1995
• Duda, Hart, Stork, "Pattern Classification", 2000
• Hastie, Tibshirani and Friedman, "Elements of Statistical Learning: Data Mining, Inference and Prediction", 2001
• MacKay, "Information Theory, Inference, and Learning Algorithms", 2003. Available on-line here

• Mitchell, "Machine Learning", 1997.
• Cover and Thomas, "Elements of Information Theory", Wiley & Sons, 1991

Stanford's Class:
Very Interesting Video Course: http://academicearth.org/courses/machine-learning

There is no required text for this course. Notes will be posted periodically on the course web site. The following books are recommended as optional reading:

• Christopher Bishop, Pattern Recognition and Machine Learning. Springer, 2006.
• Richard Duda, Peter Hart and David Stork, Pattern Classification, 2nd ed. John Wiley & Sons, 2001.
• Tom Mitchell, Machine Learning. McGraw-Hill, 1997.
• Richard Sutton and Andrew Barto, Reinforcement Learning: An introduction. MIT Press, 1998

Course handouts and other materials can be downloaded from http://www.stanford.edu/class/cs229/materials.html

Let me know if you have any opinions about these books or any I missed.