Module Handbook

  • Dynamischer Default-Fachbereich geändert auf MV

Notes on the module handbook of the department Mechanical and Process Engineering

Die hier dargestellten veröffentlichten Studiengang-, Modul- und Kursdaten des Fachbereichs Maschinenbau und Verfahrenstechnik ersetzen die Modulbeschreibungen im KIS und wuden mit Ausnahme folgender Studiengänge am 28.10.2020 verabschiedet.


Course MV-MEC-86692-K-7

Machine Learning (2V+1U, 5.0 LP)

Course Type

SWS Type Course Form CP (Effort) Presence-Time / Self-Study
- K Lecture with exercise classes (V/U) 5.0 CP 108 h
2 V Lecture 28 h
1 U Lecture hall exercise class 14 h
(2V+1U) 5.0 CP 42 h 108 h


CP, Effort 5.0 CP = 150 h
Position of the semester 1 Sem. in SuSe
Level [7] Master (Advanced)
Language [EN] English
Area of study [MV-MEC] Mechatronics in Mechanical and Automotive Engineering
Additional informations
Livecycle-State [NORM] Active


  • Bayes classifiers.
  • Logistic regression.
  • Perceptron.
  • Support vector machines.
  • Clustering.
  • Factor analysis.
  • Neural networks architecture.
  • Forward- and backpropagation.
  • Markov decision processes.
  • Bellman equations.
  • Deep Q-learning.

Competencies / intended learning achievements

1. Lecture:

Machine learning plays an important role in a variety of applications such as data mining, natural language processing, image recognition, expert systems, and autonomous driving. The lecture provides an overview of the basic concepts, techniques, and algorithms of modern machine learning. It also aims to provide rigorous mathematical foundations of the methods covered.

2. Exercise:

Students will get hands-on experience in applying machine learning algorithms on real life problems.


  • Murphy, K.: “Machine Learning: A probabilistic perspective”. Adaptive Computation and Machine Learning Series. The MIT Press, 2012.
  • Sutton, R.S., Barto, A.G.: “Reinforcement Learning: An introduction”. Cambridge: MIT Press, 1998.
  • Goodfellow I., Bengio Y., Courville A.: “Deep Learning”. Cambridge: MIT Press, 2016.
  • Hastie, T., Tibshirani, R., Friedman, J.:“The Elements of Statistical Learning: Data Mining, Inference, and Prediction”. Springer Series in Statistics, Springer, 2016.


Blackboard, auxiliary sheets. For further information and course materials please consider the corresponding OLAT-course.

Requirements for attendance (informal)

Lectures on higher mathematics

Requirements for attendance (formal)


References to Course [MV-MEC-86692-K-7]

Module Name Context
[MV-MEC-M193-M-7] Machine Learning P: Obligatory 2V+1U, 5.0 LP