Machine Learning: Supervised Methods


The field of machine learning constitutes a modern approach to artificial intelligence. It is situated in between computer science, neuroscience, statistics, and robotics, with applications ranging all over science and engineering, medicine, economics, etc.
Machine learning algorithms automate the process of learning, thus allowing prediction and decision making machines to improve with experience.

This lecture will cover a contemporary spectrum of supervised learning methods. All lecture material will be in English.

The course will use the inverted classroom concept. Students work through the relevant lecture material at home. The material is then consolidated in a 4 hours/week practical session.

Learning Outcomes:

After the successful completion of the module

  • participants understand the basics of statistical learning theory,
  • participants know the most important algorithms of supervised statistical learning and can apply them to learning problems,
  • participants know strengths and limitations of different learning methods,
  • participants are able to use standard machine learning software to solve new problems.


written exam (90 min.)



Course type
6 CP
Summer Term 2024


Takes place every week on Thursday from 10:00 to 14:00 in room IA 0/158-79 PC-Pool 1.
First appointment is on 11.04.2024
Last appointment is on 18.07.2024


The course requires basic mathematical tools from linear algebra, calculus, and probability theory. More advanced mathematical material will be introduced as needed. The practical sessions involve programming exercises in Python. Participants need basic programming experience. They are expected to bring their own devices (laptops).

This course is not available to students from TU Dortmund. Please refer to the courses offered in the scope of the data science program instead.

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