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Institute for Machine Learning
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Machine Learning: Unsupervised Techniques (1UE)

Course no.: 365.078 (group 1) / 365.095 (group 2) / 365.097 (group 3) / 365.223 (group 4)
Lecturers: Thomas Adler (adler@ml.jku.at), Markus Holzleitner (holzleitner@ml.jku.at)



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