This lecture complements (and builds on top of) the lectures "Introduction to Pattern Recognition" and "Pattern Recognition". The lecture focusses on modeling of densities, and how to use these models for analyzing the data. Major topics of this lecture are regression, density estimation, manifold learning, hidden Markov models, conditional random fields, and random forests.
|Provided by:||Universität Erlangen-Nürnberg|
How do you like the course 'Pattern Analysis 2016'?