Lecture Language:
  • German
Hours (Lecture):
  • Mo 14:15-15:45 05.025
Credit Points:
  • 2,5 ECTS
Time Lecture:
  • 2
UnivIS Links:

Machine Learning - Next Generation Methods for Signal Processing


Info PDF


The field of machine learning is concerned with the construction of intelligent systems based on the automatic discovery of regularities in data.In the learning phase, an algorithm “learns” patterns from a set of training data by adapting its model parameters. The pre-trained system is subsequently employed to analyze various input data. Machine learning is used in almost all areas of signal processing, e.g. in automatic speech recognition, parameter estimation in complex electrical networks, or in video and image processing.

In the last two decades, a variety of approaches have been proposed applying machine learning techniques to classical signal processing tasks. This seminar focuses on the description of fundamental concepts from machine learning theory as well as their application to different signal processing topics.


This seminar is designed for Bachelor and Master programs in Electrical Engineering, Electronics and Information Technology (EEI), Information and Communication Technology (IuK), Industrial Engineering and Management (WING), Computational Engineering (CE), Communications and Multimedia Engineering (CME), as well as related study programs.


The seminar is organized around three mandatory meetings:

  • Kick-off meeting – April 30, 10:00 o'clock, Room 06.025: An introduction will be given and the individual topics are assigned to the participants.
  • Mid-Term meeting – May 28th, 8:30 o'clock, Room 06.025: The participants will give a brief presentation about the status of their work and hints for the final presentation are given.
  • Seminar Workshop – July 23th, 8:30 o'clock, Room 06.025: Each participant will give a presentation of 30 minutes and a report on his/her topic of 10 pages.

The seminar is open to engineering students having completed at least 4 semesters of relevant bachelor studies. All meetings and presentations shall preferably be held in English and the reports are expected to be written in English.


Contact & Registration
Fabian Brand, M.Sc.: fabian.brand@fau.de

Nils Genser, M.Sc: nils.genser@fau.de