Recommended Texts

• Vapnik, V. N., Statistical Learning Theory , Wiley-Interscience, 1998.
• Vidyasagar, M., Learning and Generalization: With Applications to Neural Networks , Springer, e1: 1997, e2: 2003.
• Cover, T. M. and Thomas, J. A., Elements of Information Theory , Wiley-Interscience, e1: 1991, e2: 2006.
• Ljung, L., System Identification: Theory for the User , Prentice Hall, e1: 1987, e2: 1999.
• Jordan, M. and Bishop, C., Introduction to Graphical Models , (unpublished, will be made locally available.)

Additional Books

• Minsky, M. and Papert, S., Perceptrons: An Introduction to Computational Geometry, MIT Press, 1969.
• Duda, R. O., Hart, P. E., Stork, D. G., Pattern Classification , Wiley-Interscience, e1: 1973, e2: 2001
• Gyorfi, L., Kohler, M., Krzyzak, A., Walk, H., A Distribution-Free Theory of Nonparametric Regression , Springer, 2002.

Papers

• de Finetti, B., "La prévision : ses lois logiques, ses sources subjectives", Annales de l'Institut Henri Poincaré, 7, no. 1, pp. 1-68, 1937. (French, source: www.numdam.org ).
• Wainwright, M. J., and Jordan , M. I., "Graphical models, exponential families, and variational inference", UC Berkeley, Department of Statistics, Technical Report 649, Sep 2003.
• Geman, D., "Random Fields and Inverse Problems in Imaging", Lecture Notes in Mathematics, vol. 1427, pp. 113-193, Springer-Verlag, 1991.
• Hajek, B., "Cooling schedules for optimal annealing", Mathematics of Operations Research vol. 13, no. 2, pp. 311-329, May 1988.
• Tsitsiklis, J.N., "Markov chains with rare transitions and simulated annealing", Mathematics of Operations Research, vol. 14, no. 1, pp. 70-90, 1989.
• Gelfand, S.B. and Mitter, S.K., "Recursive stochastic algorithms for global optimization in \R^d", SIAM Journal on Control and Optimization, vol. 29, no. 5, pp. 999-1018, September 1991.
• Gelfand, S.B. and Mitter, S.K., "Metropolis-type annealing algorithms for global optimization in \R^d", SIAM Journal on Control and Optimization, vol. 31, no. 1, pp. 111-131, January 1993.
• Borkar, V.S., and Mitter, S.K., "A strong approximation theorem for stochastic recursive algorithms", Journal of Optimization Theory and Applications, vol. 100, no. 3, pp. 499-513, March 1999.
• Sourlas, N., "Statistical mechanics and capacity-approaching error-correcting codes", Physica A, vol. 302, p. 14-21, 2001.
• Gelfand, S.B. and Mitter, S.K., "Simulated Annealing", Advanced School on Stochastics in Combinatorial Optimization, eds. G. Andreatta, F. Mason, P. Serafini, pp. 1-51, World Scientific, 1986.