• Fundamentals of statistical pattern recognition: Bayes' rule, learning of distribution densities, linear models for classification and regression, kernel methods
• EM algorithm for maximum likelihood and Bayesian estimation
• Models with discrete and continuous hidden variables: GMM, NMF
• Bias-variance dilemma and model selection
• Graphical models
• Hidden Markov models
• Deep neural networks
• Recent publications in pattern recognition and machine learning

After attending the course, students will be able to

• select and train a suitable classifier for a given pattern recognition problem
• select a suitable approach for a given regression problem and learn the parameters on training data
• search for hidden structure in data using machine learning methods
• make a suitable choice for a model that represents a good compromise between level of detail and generalisation ability
• understand, analyse and evaluate recent publications in the field of pattern recognition and machine learning

The students

• have an understanding of the importance of the choice of model order on the quality of classification and regression
• have an understanding of the fact that the search for hidden variables is based on a priori assumptions that can strongly influence the result
• are able to familiarise themselves independently with the state of research in sub-areas of pattern recognition and machine learning by researching and studying the literature
• are able to categorise publications from this field in a larger context
• can transfer the knowledge and skills gained in this course to other disciplines

• Lectures with predominantly blackboard use, occasional slide presentation
• Classroom exercises with exercise sheets and demonstrations on the computer
• Instructions on how to analyse current scientific publications
• and subsequent independent familiarisation with specialist literature by the students
• Presentation of current publications by the students

• R.O. Duda, P.E. Hart, D.G.~ Stork, Pattern Classification, Wiley, 2001
• K. Fukunaga, Introduction to Statistical Pattern Recognition, Academic Press, 1990
• C. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006
• C. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006

• Course for Master students in Electrical Engineering, Computer Engineering and Electrical Systems Engineering
• ECTS: 6
• Language: German or English (depending on preference)
• Semester: Winter semester

Lecture slides

• Introduction
• Regression
• Information Theory and Machine Learning
• Discriminative Training
• EM Algorithm and Variational Inference
• Nonnegative Matrix Factorisation
• Deep Neural Networks - Part 1
• Deep Neural Networks - Part 2
• ASR
• Sequential Data
• Ensemble Learning and Decision Trees
• Seminar Topics 2019/2020
• Paper on hierarchical NMF

Other

• Neural Network trained with CE approaches true class posterior

Templates and Leaflets

• Thesis
• Presentation
• Leaflet on Plagiarism

Exercises:

• To the exercise materials