Fundamentals of Machine Learning


This is an advanced course intended for master students in mechanical engineering who are interested in machine learning and data science.

Course Description

The class Fundamentals of Machine Learning (formerly, Computer Science in Mechanical Engineering II) covers state-of-the-art data science methods, and how to apply them to problems relevant to mechanical engineering. The course will cover the theoretical basics in probability theory, then move on to methods of modern machine learning. The class emphasizes both solid theoretical understanding of the algorithms, as well as hands-on programming exercises to apply them to problems in the context of engineering.



  • Cycle: Winter semester
  • Assessment: Written Exam, 120 minutes
  • Credits: 6 CP (5 CP for Robotic Systems Engineering)
  • Language: English

Class Outine

  • Introduction to probability theory
  • Bayes principle
  • Linear Models for Regression and Classification
  • Neural networks
  • Gaussian processes

Lectures and Exercises

The course is given as L2/E2 during the winter term and is held in English. For information on lectures and exercises check with RWTHonline. Course materials for the lectures and the exercises can be downloaded from the RWTHmoodle platform.

Consultation Hours

Consultation hours are offered weekly on appointment