194.101 Machine Learning Algorithms and Applications
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2020W, PR, 2.0h, 3.0EC


  • Semester hours: 2.0
  • Credits: 3.0
  • Type: PR Project
  • Format: Distance Learning

Learning outcomes

After successful completion of the course, students are able to...

  • read and summarise research papers (if applicable);
  • derive the needed information to (re)implement learning methods;
  • develop implementations/applications of learning algorithms;
  • empirically evaluate (and experiment with) machine learning algorithms to:
        - identify appropriate hyperparameters for the algorithm;
        - compare different learning algorithms and analyse their strengths and weaknesses;
        - apply them to data.


Subject of course

The goal of this project is to understand and (re)implement learning algorithms. Besides that, you will experiment with various hyperparameter and data sets. You can find some suggestions for projects on our homepage. If you have your own creative and specific project idea (check criteria on homepage), we are also happy to supervise you.

Teaching methods

The main part of this project consists of implementing existing learning algorithms. During the project, the students will continuously give progress presentations. Finally, the students will give a final presentation and write a report, to sum up the outcome.

Mode of examination




Examination modalities

The final grade is made up of the quality of

  • the implementation (runnability, scalability, runtime, documentation),
  • the presentations,
  • and the written report.

Course registration

Begin End Deregistration end
14.09.2020 00:00 07.10.2020 23:59



No lecture notes are available.