Statistical Pattern Recognition
Prof. Thomas BroxStatistical pattern recognition, also known as "machine learning", is a 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 Python.
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.
The lecture is planned to be given in presence. Nonetheless, there is a good tradition to record the lecture, so that you can also participate remotely. The exercise sessions will be via an online meeting for everybody can participate, but also a room is reserved for that time, i.e., you can meet there to communicate with fellow students. You must be present for the exam.
Please register for the course in time for we have your email address and can send you updates like logins, etc.

Slides and Recordings
Date  Topic  Slides  Recordings  Solutions 

27.4.  Class 1: Introduction  MachineLearning01.pdf  MachineLearning01.mp4  
4.5.  Class 2: Probability distributions  MachineLearning02.pdf  MachineLearning02.mp4  
11.5.  Class 3: Mixture models, clustering, and EM  MachineLearning03.pdf  MachineLearning03.mp4  
18.5.  Class 4: Nonparametric methods  MachineLearning04.pdf  MachineLearning04.mp4  
25.5.  Class 5: Regression  MachineLearning05.pdf  MachineLearning05.mp4  
1.6.  Class 6: Gaussian processes  MachineLearning06.pdf  MachineLearning06.mp4  
15.6.  Class 7: Classification  MachineLearning07.pdf  MachineLearning07.mp4  
22.6.  Class 8: Support vector machines  MachineLearning08.pdf  MachineLearning08.mp4  
29.6.  Class 9: Projection methods  MachineLearning09.pdf  MachineLearning09.mp4  
6.7.  Class 10: Inference in graphical models  MachineLearning10.pdf  MachineLearning10.mp4  
13.7.  Class 11: Sampling methods  MachineLearning11.pdf 
MachineLearning11.mp4 
Exercises
The exercise material is provided at a Github repository.There is an Online Forum for announcements, questions, and discussions.