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04.03.2024 16:52:01
Lecture: 5.04.4213 Machine Learning I - Probabilistic Unsupervised Learning - Details
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General information

Course name Lecture: 5.04.4213 Machine Learning I - Probabilistic Unsupervised Learning
Course number 5.04.4213
Semester WiSe21/22
Current number of participants 79
expected number of participants 20
Home institute Institute of Physics
Courses type Lecture in category Teaching
First date Wednesday, 20.10.2021 10:15 - 11:45, Room: W32 0-005
Type/Form V, Ü
Pre-requisites Basic knowledge in higher Mathematics as taught as part of first degrees in Physics, Mathematics, Statistics, Engineering or Computer Science (basic linear algebra and analysis). Basic programming skills (course supports matlab & python). Many relations to statistical physics, statistics, probability theory, stochastic but the course's content will be developed independently of detailed prior knowledge in these fields.
Performance record Exam or presentation or oral exam or homework or practical report
Lehrsprache englisch
ECTS points 6

Rooms and times

W32 0-005
Wednesday: 10:15 - 11:45, weekly (14x)
W01 0-012
Tuesday, 08.03.2022 - Thursday, 10.03.2022 08:30 - 18:30
W04 1-171
Friday, 11.03.2022 08:30 - 18:30
Monday, 14.03.2022 08:30 - 16:00
Tuesday, 15.03.2022 08:30 - 13:00
No room preference
Monday, 30.05.2022 11:00 - 18:00
Tuesday, 31.05.2022 08:00 - 18:00

Module assignments


The field of Machine Learning develops and provides methods for the analysis of data and signals. Typical application domains are computer hearing, computer vision, general pattern recognition and large-scale data analysis (recently often termed "Big Data"). Furthermore, Machine Learning methods serve as models for information processing and learning in humans and animals, and are often considered as part of artificial intelligence approaches.

This course gives an introduction to unsupervised learning methods, i.e., methods that extract knowledge from data without the requirement of explicit knowledge about individual data points. We will introduce a common probabilistic framework for learning and a methodology to derive learning algorithms for different types of tasks. Examples that are derived are algorithms for clustering, classification, component extraction, feature learning, blind source separation and dimensionality reduction. Relations to neural network models and learning in biological systems will be discussed were appropriate.

The course requires some programming skills, preferably in Matlab or Python. Further requirements are typical mathematical / analytical skills that are taught as part of Bachelor degrees in Physics, Mathematics, Statistics, Computer and Engineering Sciences. Course assignments will include analytical tasks and programming task which can be worked out in small groups.

The presented approach to unsupervised learning relies on Bayes' theorem and is therefore sometimes referred to as a Bayesian approach. It has many interesting relations to physics (e.g., statistical physics), statistics and mathematics (analysis, probability theory, stochastic) but the course's content will be developed independently of detailed prior knowledge in these fields.


Admission settings

The course is part of admission "Anmeldung gesperrt (global)".
Erzeugt durch den Stud.IP-Support
The following rules apply for the admission:
  • Admission locked.