Required book for the course:

• Stephen Marsland, Machine Learning - An Algorithmic Perspective CRC Press, 2009. Book's Home Page

The book by Marseland is thin and manageable, yet a little light on the 'why' things work. It is enough for getting to know the algorithms out there though! There are a number of other books that go a bit deeper:

• Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer 2007. Book's Home Page
• Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning, 2nd edition, Springer 2009. Book's Home Page
• Xindong Wu, Vipin Kumar, The Top Ten Algorithms in Data Mining, Chapman & Hall/CRC Data Mining and Knowledge Discovery Series, 2009. Book's Home Page
• Vladimir Cherkassky, Filip M. Mulier, Learning from Data: Concepts, Theory, and Methods, 2nd edition, Wiley-IEEE Press 2007. Book's Home Page