| Detailinformationen |
| Quellcurriculum |
Bachelorstudium Artificial Intelligence 2019W |
| Ziele |
(*)This practical course complements the lecture "Machine Learning: Unsupervised Techniques" and aims at practicing the concepts and methods acquired in the lecture.
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| Lehrinhalte |
(*)- Error models
- Information bottleneck
- 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
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| Beurteilungskriterien |
(*)Marking is based on homework
|
| Lehrmethoden |
(*)Students are given assignments in 1-2 week intervals. Homework must be handed in. Results are to be presented and discussed in the course.
|
| Abhaltungssprache |
Englisch |
| Literatur |
(*)Assignments and homework submissions are managed via JKU Moodle.
Where necessary, complimentary course material is provided for download.
|
| Lehrinhalte wechselnd? |
Nein |
| Äquivalenzen |
(*)875BIMLMUTU16: UE Machine Learning: Unsupervised Techniques (1,5 ECTS)
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| Frühere Varianten |
Decken ebenfalls die Anforderungen des Curriculums ab (von - bis)
875BIMLMUTU16: UE Machine Learning: Unsupervised Techniques (2016W-2019S)
875BIN2MUTU13: UE Machine Learning: Unsupervised Techniques (2013W-2016S)
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