Statistical Pattern Recognition

Prof. Thomas Brox

Statistical 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 to the fundamentals of machine learning and its major tasks: 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 non-dog 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. It puts emphasis on the probabilistic modelling, and thus goes a bit deeper into the fundamentals of machine learning than other machine learning introductory lectures. The exercises will consist of theoretical assignments and programming assignments in Python.

Lecture: (2 SWS)	Monday, 10:15-11:45 101-01-016/18
Exercises: (2 SWS)	Monday, 16:00 101-01-016/18 Contact persons: Sudhanshu Mittal, Silvio Galesso Discussion Forum Exercise sessions happen on the same dates as lectures (see timetable below). If there is no lecture, there is also no exercise session on that day.
Beginning:	Lecture: Monday, April 28, 2025 Exercises: Monday, April 28, 2025
ECTS Credits:	6
Recommended semester:	1 or 2 (MSc)
Requirements:	Fundamental mathematical knowledge, particularly statistics.
Exam:	Written exam on Aug. 6 14:00-15:00 in Building 101. It will consist of a mixture of binary choice questions and fields, in which you must fill your solution. To get an idea of the style of the exam you can have a look at the test exam for image processing and the test exam for optimization.-->
Remarks:	Full completion of all relevant theoretical and programming assignments is highly recommended.

Slides and Recordings

Date	Topic	Slides	Recordings
28.4.	Class 1: Introduction	MachineLearning01.pdf	MachineLearning01.mp4
5.5.	Class 2: Probability distributions	MachineLearning02.pdf	MachineLearning02.mp4
12.5.	Class 3: Mixture models, clustering, and EM	MachineLearning03.pdf	MachineLearning03.mp4
19.5.	Class 4: Nonparametric methods	MachineLearning04.pdf	MachineLearning04.mp4
26.5.	Class 5: Regression	MachineLearning05.pdf	MachineLearning05.mp4
2.6.	Class 6: Gaussian processes	MachineLearning06.pdf	MachineLearning06.mp4
23.6.	Class 7: Classification	MachineLearning07.pdf	MachineLearning07.mp4
30.6.	Class 8: Support vector machines	MachineLearning08.pdf	MachineLearning08.mp4
7.7.	Class 9: Projection methods	MachineLearning09.pdf	MachineLearning09.mp4
14.7.	Class 10: Inference in graphical models	MachineLearning10.pdf	MachineLearning10.mp4
21.7.	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.

See the Short introduction to git if you have never used git before.