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Classification based on Bayes decision theory (basic principles; Bayes
classifiers for normal distributions; estimation of probability
density: maximum likelihood estimation, maximum a posteriori
probability, maximum entropy). Bayesian networks. Linear classifiers
(single layer perceptron, LMS algorithm, support vector machines).
Non-linear classifiers (decision trees, multilayer perceptrons, radial
basis functions, non-linear support vector machines). Context based
classification (Markovian chains, Viterbi algorithm, hidden Markov
models). Introduction to feature selection and extraction (statistical
hypothesis testing, search methods, principal component analysis,
linear discriminant analysis, moments, discrete Fourier transform,
wavelets). Introduction to clustering (examples of clustering
algorithms: serial algorithms, isodata, self organizing maps). Pattern
matching (Bellman's optimality principle, dynamical programming,
Levenshtein distance).
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