Machine Learning: Unsupervised Techniques (1UE)

Course no.: 365.078 (group 1) / 365.095 (group 2)
Lecturers: Johannes Brandstetter, Johannes Kofler


This practical course complements the lecture Machine Learning: Unsupervised Techniques and aims at practicing the concepts and methods acquired in the lecture.


  • Error models
  • Maximum likelihood and the expectation maximization algorithm
  • Maximum entropy methods
  • Basic clustering methods, hierarchical clustering, and affinity propagation
  • Mixture models
  • Principal component analysis, independent component analysis, and other projection methods
  • Factor analysis
  • Matrix factorization
  • Auto-associator networks and attractor networks
  • Boltzmann and Helmholtz machines
  • Hidden Markov models
  • Belief networks
  • Factor graphs