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  • Machine Learning: Unsupervised Methods (with Problem Based Learning)

Machine Learning: Unsupervised Methods (with Problem Based Learning)



Course type
6 CP
Winter Term 2020/2021
moodle course available


Takes place every week on Tuesday from 12:15 to 13:45 in room online.
First appointment is on 27.10.2020
Last appointment is on 09.02.2021
Takes place every week on Tuesday from 10:30 to 12:00 in room online.
First appointment is on 27.10.2020
Last appointment is on 09.02.2021

Teaching format (Lehrformen): This course is given with the problem based learning concept. The students work in groups of about 5 on realistic problems that can be solved with unsupervised learning methods from machine learning. They develop hypotheses and strategies for a solution and identify what they need to learn in order to implement these. Thus the students will not only learn about machine learning but also soft skills.

Requirements: 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.

Content (Inhalt): This course covers a variety of unsupervised methods from machine learning such as principal component analysis, independent component analysis, vector quantization, clustering, Bayesian theory and graphical models.

Learning outcomes (Lernziele): After the successful completion of this course the students
  • know a number of important unsupervised learning methods,
  • can discuss and decide which of the methods are appropriate for a given data set,
  • understand the mathematics of these methods,
  • know how to implement and apply these methods in python,
  • have gained experience in organizing and working in a team,
  • know problem solving strategies like brain storming,
  • can communicate about all this in English.

Exam (Prüfungsformen): As an exam prerequisite (Prüfungsvorleistung) participants have to structure and document their learning progress and contribution to the group work, including setting personal testable milestones. The course is concluded with a ca 20 min graded oral exam. The dates are set at the end of the semester.

Condition for granting the credit points (Voraussetzungen für die Vergabe von Kreditpunkten): Exam prerequisite and passed oral exam

Course Material: Please download all material you need until end of July, because beginning in August I prepare the new course and some links of the old course might break.


Link Moodle course page: https://moodle.ruhr-uni-bochum.de/m/course/view.php?id=26279

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210