Pennsylvania State University

Recommended Textbooks

1. Abu-Mostafa, Y., Magdon-Ismail, M., and Lin, H-T. (2012). Learning from Data. AMLBook.com
2. 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.
3. 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.
4. Barber, D. (2012). Bayesian Reasoning and Machine Learning. Cambridge University Press.
   An online text on machine learning, with emphasis on graphical models.
5. Bishop, C. M. Neural Networks for Pattern Recognition. New York: Oxford University Press (1995).
   This book offers a good coverage of neural networks
6. Chakrabarti, S. (2003). Mining the Web, Morgan Kaufmann.
   
7. 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.
8. Cowell, R.G., Dawid, A.P., Lauritzen, S.L., and Spiegelhalter,D.J. (1999). Graphical Models and Expert Systems.Berlin: Springer. jbr> This is a very good introduction to probabilistic graphical models.
9. 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.
   
10. Devroye, Luc, Györfi, László, Lugosi, Gabor. (1996). A probabilistic theory of pattern recognition. Springer.
11. 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.
12. 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.
13. Leskovec, U., Rajaraman, A., and Ullman, J. (2014). Mining Massive Data Sets
    Online text focusing on mining large data sets
14. 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.
15. Koller, D. and Friedman, N. (2009). Probabilistic Graphical Models. MIT Press.
16. Mitchell, T. (1997). Machine Learning. New York: Mc Graw-Hill.
    This is, although a bit dated, an excellent introduction to Machine Learning.
17. Mohri, M., Rostamzadehm A., and Talwalker, A. (2012). Foundations of Machine Learning MIT Press, 2012.
18. Murphy, K. (2012). Machine Learning: A probabilistic perspective. MIT Press.
    An accessible survey of machine learning from a probabilistic perspective.
19. Natarjan, B. (1991). Machine Learning: A Theoretical Approach. Kluwer.
20. Neapolitan, R. (2004). Learning Bayesian Networks. Prentice-Hall.
21. 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.
22. Skolkopf, B. ad Smola, A. (2001). Learning with Kernels. MIT Press.
23. Tan, P-N., Steinbach, M., and Kumar, V. (2004). Introduction to Data Mining. New York: Addison-Vesley.
24. Theodoridis, S. (2015). Machine Learning. Springer.
    A bayesian and optimization perspective on machine learning.
25. Vapnik, V. (1998). Statistical Learning Theory. Wiley.
    An excellent coverage of structural risk minimization approach to machine learning.
26. Vidyasagar, M. (2002). A theory of learning and generalization, with applications to Neural Networks. Springer.
    An excellent book covering the theoretical foundations of machine learning and generalization.

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.