101.722 AKANW Project Machine Learning: Theory 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.

2022S, PR, 5.0h, 7.5EC

Properties

  • Semester hours: 5.0
  • Credits: 7.5
  • Type: PR Project
  • Format: Hybrid

Learning outcomes

After successful completion of the course, students are able to understand the theory of certain methods in machine learning and to implement corresponding algorithms. Selected timely application problems are solved in this manner.

Subject of course

This time there will be a focus on reinforcement learning (RL) and timely applications such as:

  • deep RL,
  • policy evaluation,
  • reliability,
  • explainability,
  • RL with function approximation in scientific and medical applications,
  • autonomous driving,
  • algorithms for computer games (Atari 2600, ALE (arcade learning environment)),
  • algorithms for games such as chess and Go,
  • algorithms for card games,
  • etc.

Theory: convergence and complexity of various algorithms.

Teaching methods

Methods of reinforcement learning, supervised, and unsupervised learning.

Mode of examination

Immanent

Additional information

Time of first meeting will be announced.

Lecturers

Institute

Examination modalities

Implementation and documentation.

Course registration

Not necessary

Curricula

Literature

No lecture notes are available.

Previous knowledge

Linear algebra, analysis.  Experience in programming languages such as Julia or Python.

Miscellaneous

Language

if required in English