194.100 Theoretical Foundations and Research Topics in 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.

2021S, VU, 2.0h, 3.0EC
Lecture TubeTUWEL


  • Semester hours: 2.0
  • Credits: 3.0
  • Type: VU Lecture and Exercise
  • LectureTube course
  • Format: Distance Learning

Learning outcomes

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

  • explain the theoretical foundations of machine learning;
  • prove learning theoretical results and algorithmic properties of machine learning;
  • apply learning algorithms correctly;
  • compare and analyse learning algorithms; and
  • understand, summarise, and present machine learning research papers.

Subject of course

This lecture introduces theoretical foundations and advanced topics in machine learning. We analyse learning algorithms and show provable guarantees, such as (probabilistic) bounds on the predictive performance.

Tentative topics:

  • Statistical learning theory
  • Kernel-based learning
  • Online learning
  • Clustering
  • Semi-supervised learning

Teaching methods

A mix of introductory online lectures (recorded and/or live), recorded online talks (like conference and summer school tutorials), and exercises with formaitve feedback and some live online sessions where the assigments are discussed.

Mode of examination


Additional information

3ects -> 75h
20h (re)viewing lectures and lecture materials
10h (re)viewing background material
10h exercises
20h coursework
15h Final project



Examination modalities

The final grade consists of

  • regularly submitted written coursework and
  • a larger final project, such as a written report or a (recorded or online presented) talk

Course registration

Begin End Deregistration end
01.03.2021 00:00 29.06.2021 23:59 30.06.2021 23:59



No lecture notes are available.