Statistical Pattern Recognition
Prof. Thomas BroxStatistical pattern recognition, nowadays often known under the term "machine learning", is the key element of modern computer science. Its goal is to find, learn, and recognize patterns in complex data, for example in images, speech, biological pathways, the internet. In contrast to classical computer science, where the computer program, the algorithm, is the key element of the process, in machine learning we have a learning algorithm, but in the end the actual information is not in the algorithm, but in the representation of the data processed by this algorithm.
This course gives an introduction in all tasks of machine learning: classification, regression, and clustering. In the case of classification, we learn a decision function from annotated training examples (e.g., a set of dog and nondog images). Given a new image, the classifier should be able to tell whether it is a dog image or not. In regression we learn a mapping from an input function to an output function. Again this mapping is learned from a set of input/output pairs. Both classification and regression are supervised methods as the data comes together with the correct output. Clustering is an unsupervised learning method, where we are just given unlabeled data and where clustering should separate the data into reasonable subsets. The course is based in large parts on the textbook "Pattern Recognition and Machine Learning" by Christopher Bishop. The exercises will consist of theoretical assignments and programming assignments in Matlab.
The content of this course is complementary to the Machine Learning course offered by Joschka Boedecker and Frank Hutter. It absolutely makes sense to attend both courses if you want to specialize in Machine Learning. It also complements the Deep Learning course.

Slides and Recordings
Additional information and some helpful hints regarding the exercises can be found here.
Recordings from Olaf Ronneberger (2015):
Class 1:  Introduction 
Class 2:  Probability distributions 
Class 3:  Mixture models, clustering, and EM 
Class 4:  Nonparametric methods 
Class 5:  Regression 
Class 7:  Classification 
Class 8:  Support vector machines 
Class 9:  Deep Learning 
Class 10:  Projection methods 
Class 11:  Sampling methods 