Course Jukebox

Course Jukebox

Course Detail

Standard Academic Year
Course delivery methods
Online (Synchronous)
Computer Science, Engineering & technology
Faculty of Computer Science
Course Offering Year
Course Offering Month
October - February
Weekday and Period
Weekly contact sessions Tuesdays 10:30-13:45 CET from 11.10.2022 till 31.1.2023. Exam: Presentations during contact sessions and online quizzes.
Anticipated number of participants: 50 Slots available for CiC students: 5
Course Number

Machine Learning: Unsupervised Methods (with Problem Based Learning) Ruhr-Universität Bochum

Course Overview

​This course covers a variety of shallow unsupervised methods from machine learning such as principal component analysis, independent component analysis, vector quantization, clustering, Bayesian theory and graphical models.

Learning Achievement


Course prerequisites

Grading Philosophy

The mathematical level of the course is mixed but generally high, including calculus (functions, derivatives, integrals, differential equations, ...), linear algebra (vectors, matrices, inner product, orthogonal vectors, basis systems, ...), and a bit of probability theory (probabilities, probability densities, Bayes' theorem, ...). Programming is done in Python, thus the students should have a basic knowledge of that as well, or at least be fluent in another programming language.

Course schedule

Course type

Online Course Requirement


Prof. Laurenz Wiskott

Other information

This course is given in a hybrid of conventional lectures, inverted classroom, and problem based learning. The course starts with a two-week introduction into unsupervised methods of machine learning, providing an overview. The students then work in groups of about 4 on realistic problems that can be solved with these methods. In the first week of a problem, they develop hypotheses and strategies for a solution and identify which methods they want to learn. Then the course agrees on a method to focus on theoretically, which will then be done in an inverted classroom format. The students then try to solve the problem and present their results in a short talk with slides recorded as a video. Thus the students will not only learn about machine learning but also soft skills.
Programme: Applied Computer Science

Site for Inquiry

Please inquire about the courses at the address below.

Email address:

Link to the syllabus provided by the university