Course no.: | |
---|---|
Lecturers: | |
365.078 (group 1) | Johannes Brandstetter, Johannes Kofler |
Times/locations: | |
365.078 (group 1) | Mon 14:30-15:15, room MT 226 Start: Mon, March 4, 2019 |
Course no.: | |
365.078 (group 1) | 365.095 (group 2) |
Lecturers: | |
365.078 (group 1) | Johannes Brandstetter, Johannes Kofler |
Times/locations: | |
365.078 (group 1) | Mon 13:45-14:30, room MT 226 Start: Mon, March 4, 2019 |
Mode: | |
365.078 (group 1) | UE, 1h, weekly |
Registration: | |
365.078 (group 1) | KUSSS, öffnet eine externe URL in einem neuen Fenster |
Course no.: | |
365.078 (group 1) | 365.097 (group 3) |
Lecturers: | |
365.078 (group 1) | Johannes Brandstetter, Johannes Kofler |
Times/locations: | |
365.078 (group 1) | Mon 14:30-15:15, room MT 226/1 Start: Mon, March 4, 2019 |
Mode: | |
365.078 (group 1) | UE, 1h, weekly |
Registration: | |
365.078 (group 1) | KUSSS, öffnet eine externe URL in einem neuen Fenster |
Course no.: | 365.078 (group 1) |
Lecturers: | Johannes Brandstetter, Johannes Kofler |
Times/locations: | Mon 14:30-15:15, room MT 226 Start: Mon, March 4, 2019 |
Course no.: | 365.095 (group 2) |
Lecturers: | Johannes Brandstetter, Johannes Kofler |
Times/locations: | Mon 13:45-14:30, room MT 226 Start: Mon, March 4, 2019 |
Mode: | UE, 1h, weekly |
Registration: | KUSSS, öffnet eine externe URL in einem neuen Fenster |
Course no.: | 365.097 (group 3) |
Lecturers: | Johannes Brandstetter, Johannes Kofler |
Times/locations: | Mon 14:30-15:15, room MT 226/1 Start: Mon, March 4, 2019 |
Mode: | UE, 1h, weekly |
Registration: | KUSSS, öffnet eine externe URL in einem neuen Fenster |
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