The recommended textbook for the course is:

Bishop, C. (2006). Pattern Recognition and Machine Learning. Berlin: Springer-Verlag.

Additional references are:

1. Baldi, P. and Brunak, S. (2002). Bioinformatics: A Machine Learning Approach. Cambridge, MA: MIT Press.
   This book offers a good coverage of machine learning approaches - especially neural networks and hidden Markov models in bioinformatics.
2. Baldi, P., Frasconi, P., Smyth, P. (2003). Modeling the Internet and the Web - Probabilistic Methods and Algorithms. New York: Wiley.
   A good introduction to machine learning approaches to text mining and related applications on the web.
3. Bishop, C. M. Neural Networks for Pattern Recognition. New York: Oxford University Press (1995).
   This book offers a good coverage of neural networks
4. Chakrabarti, S. (2003). Mining the Web, Morgan Kaufmann.
   
5. Cohen, P.R. (1995) Empirical Methods in Artificial Intelligence. Cambridge, MA: MIT Press. This is an excellent reference on experiment design, and hypothesis testing, and related topics that are essential for empirical machine learning research.
6. Cowell, R.G., Dawid, A.P., Lauritzen, S.L., and Spiegelhalter,D.J. (1999). Graphical Models and Expert Systems.Berlin: Springer.
   This is a very good introduction to probabilistic graphical models.
7. Cristianini, N. and Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines. London: Cambridge University Press.
   This is an excellent introduction to kernel methods for pattern classification.
   
8. Duda, R., Hart, P., and Stork, D. (2001). Pattern Classification. New York: Wiley.
   This is a good text with primary emphasis on statistical methods for pattern classification.
9. Hastie, T., Tibshirani, R., and Friedman, J. (2001). The elements of Statistical Learning - Data Mining, Inference, and Prediction. Berlin: Springer-Verlag.
   This is an excellent text that explains some of the key ideas in machine learning within a statistical framework.
10. Jordan, M. (2003). Probabilistic Graphical Models. Professor Jordan has kindly shared a pre-publication draft.
    This text has an excellent coverage of generative and discriminative probabilistic models for classification.
11. Kearns, M. and Vazirani, U. (1994). Computational Learning Theory. Cambridge, MA: MIT Press.
    This, although a bit dated, is an excellent introduction to learning theory.
12. Mitchell, T. (1997). Machine Learning. New York: Mc Graw-Hill.
    This is, although a bit dated, an excellent introduction to Machine Learning.
13. Russel, S. and Norvig, P. (2003). Artifiical Intelligence: A Modern Approach. 2nd Edition. New York: Prentice-Hall.
    This is an excellent text on Artificial Intelligence, with several introductory chapters on Machine Learning.
14. Tan, P-N., Steinbach, M., and Kumar, V. (2004). Introduction to Data Mining. New York: Addison-Vesley.

In addition, we will draw on a number of primary sources for material to be covered in this course. See the weekly study guide for pointers.