Machine Learning: Unsupervised Techniques (1UE)
| Course no.: | 365.078 |
| Lecturers: | Andreas Mitterecker Andreas Mayr Thomas Unterthiner |
| Times/locations: | Mon 14:30-15:15, room S2 059 Start: Mon, March 3, 2014 |
| Mode: | UE, 1h, weekly |
| Registration: | KUSSS |
Motivation:
This practical course complements the lecture Machine Learning: Unsupervised Techniques and aims at practicing the concepts and methods acquired in the lecture. Topics:- 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