machine learning methods

bayesian decision theory (bayes estimation, bayes network),
maximum-likelihood and bayesian parameter estimation,
EM (Expectation-Maximization),
hidden markov model,
Parzen window,
nearrest-neighbor estimation,
reduced coulomb energy network,
linear discriminant functions
multilayer neural networks
stochastic serach, boltzmann learning
evolutionary methods (genetic algorithm, genetic programming)
hierarchical clustering,
component analysis(PCA, NLCA, ICA),
k-means clustering
self-organization map,
support vector machine,
learning automata,
reinforcement learning(TD, Q-learning, dynammic programming, monte carlo
methods etc.)
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