Readings available in the electronic form:

• Machine learning and data mining overview
  T. Mitchell. Machine learning and data mining. Communications of the ACM, 42-11, 1999.

• Support vector machines
  C. J.C. Burgess. A tutorial on support vector machines for pattern recognition. Machine Learning journal, 1998.

• Learning Bayesian belief networks
  D. Heckerman A tutorial on learning with Bayesian belief networks. (sections 1-5, 7-13) MSR-TR-95-06, 1996.

• Expectation-maximization
  A.P. Dempster, N.M. Laird, D.B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of Royal statistical society, vol. 39, issue 1, pp. 1-28, 1977
  

• Mixture of experts:
  Michael Jordan.  Hierarchical mixtures of experts and the EM algorithm.  Neural Computation, 6, pp. 181-214, 1994.
  

• Ensamble methods. Bagging and boosting.
  L. Breiman. Arcing classifiers.
  Y. Freund, R. Schapire. Experiments with a New Boosting algorithm. In the Proceedings of the 13-th International  Conference on Machine Learning, 1996.
  R.E.  Schapire, Y. Freund, P. Barlett, W.S. Lee. Boosting the Margin: A new explanation for the effectiveness of voting methods. The Annals of Statistics, vol.26, pp. 1651-1686, 1998.
  

• Reinforcement Learning
  L.P. Kaelbling, M.L. Littman, A. W. Moore.  Reinforcement learning: a survey.  Journal of Artificial Intelligence Research, 1996.