Pattern Recognition II

The lecture deals with the basics and methods of pattern recognition and machine learning, especially from a probabilistic point of view. The following topics will be discussed: Advanced Neural Networks (including CNN, RBF Networks), Gaussian Processes, Bayesian Networks and Markov Random Fields, Abstract Views on Expectation Maximization and Variation Inference, Sampling Techniques, Continuous Deferred Variables (Principal Component Analysis) , Sequential Data Processing with Hidden Markov Models, Ensemble Techniques