The aim of this course is to make students understand the theoretical background of the pattern recognition methods and apply this knowledge to realistic practical applications. The cource topics: Bayesian Decision Theory, Bayesian Classification for Normal Distributions, Maximum Likelihood Parameter Estimation, Linear Discriminant Functions, Linear Support Vector Machines/kernel SVMs, Decision Tree Learning/random forests, Data Preprocessing/dimensionality reduction, Model evaluation/cross validation, Clustering, Multilayer Neural Networks