Statistical Pattern Recognition

The course is lectured biannually, next time spring 2018.

The course deals with statistical methods for classification and clustering.

Within classification focus is set on Bayesian theory, parametric and non-parametric techniques, different estimation methods, distortion measures, linear and nonlinear classifiers including deep neural networks, different classifier structures, static and dynamic problems, generalization, etc.

Within clustering focus is set upon hierarchical methods, classical algorithms like K-means, newer techniques like fuzzy and competitive methods, latent semantic analysis, etc. Further, choice of distortion measures and optimization criteria matched to input room topology.

Learning objectives : The student shall learn the theory of statistical methods for classification and clustering. This applies to both basis theory and state-of-the-art methods.

Skills : The student shall learn how to apply the theory on different physical signals like images, speech, medical signals aso.

A combination of lectures and self-study.