There is no textbook for this course. All the required information will be presented in the slides associated with each class. The books and articles listed below are useful general reference reading, especially from the theoretical viewpoint. Additional readings are listed in the lecture note PDF files.
Cristianini, N., and J. Shawe-Taylor. Introduction To Support Vector Machines. Cambridge, 2000.
Cucker, F., and S. Smale. “On The Mathematical Foundations of Learning.” Bulletin of the American Mathematical Society. 2002.
Devroye, L., L. Gyorfi, and G. Lugosi. A Probabilistic Theory of Pattern Recognition. Springer, 1997.
Evgeniou, T., M. Pontil, and T. Poggio. “Regularization Networks and Support Vector Machines.” Advances in Computational Mathematics. 2000.
Poggio, T., and S. Smale. “The Mathematics of Learning: Dealing with Data.” Notices of the AMS. 2003.
Vapnik, V. N. The Nature of Statistical Learning Theory. Springer, 1995.
———. Statistical Learning Theory. Wiley, 1998.
