Pattern Recognition I

The lecture deals with fundamentals and method of pattern recognition, in particular from a probabilistic view. The following topics will be discussed: basics (including stochastics, model selection, course of dimensionality, decision and information theory), distributions (i.a. multinomial, dirichlet, gauss and student distribution, nonparametric estimation), linear models for regression, linear models for classification, mixed models and expectation maximization, approximative inference, combination of models, statistical learning theory (support vector machines), example applications (online clustering, anomaly detection, etc.)