101.696 Machine learning
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2021W, SE, 2.0h, 3.0EC


  • Semester hours: 2.0
  • Credits: 3.0
  • Type: SE Seminar
  • Format: Distance Learning

Learning outcomes

After successful completion of the course, students are able to search the literature on their own, to write seminar papers using the LaTeX typesetting system, and to prepare and give presentations (45 minutes).

Subject of course

  • Recent topics in supervised learning
  • Recent topics in unsupervised learning
  • Generative adversarial networks (GAN)
  • Reinforcement learning: various topics
  • Reinforcement learning: Atari, Go, etc.
  • Artificial neural networks, deep learning
  • Bayesian estimation (for PDE): numerical algorithms
  • Bayesian estimation (for PDE): theory
  • Image classification
  • Autonomous driving
  • Etc.

Teaching methods

Supervised machine learning, unsupervised machine learning, reinforcement learning.

Mode of examination

Written and oral

Additional information

Please consider the plagiarism guidelines of TU Wien when writing your seminar paper: Directive concerning the handling of plagiarism (PDF)



Examination modalities

Semianr paper and seminar presentation.

Course registration

Not necessary



No lecture notes are available.

Previous knowledge

Linear algebra, analysis.


if required in English