Inhalt

[ 875BIMLMUTU16 ] UE (*)Machine Learning: Unsupervised Techniques

Versionsauswahl
(*) Leider ist diese Information in Deutsch nicht verfügbar.
Workload Ausbildungslevel Studienfachbereich VerantwortlicheR Semesterstunden Anbietende Uni
1,5 ECTS M1 - Master 1. Jahr Bioinformatik Sepp Hochreiter 1 SSt Johannes Kepler Universität Linz
Detailinformationen
Quellcurriculum Masterstudium Bioinformatics 2016W
Ziele (*)This practical course complements the lecture "Machine Learning: Unsupervised Techniques" and aims at practicing the concepts and methods acquired in the lecture.
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
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 (*)875BIN2MUTU13: UE Machine Learning: Unsupervised Techniques (1,5 ECTS)
Präsenzlehrveranstaltung
Teilungsziffer 35
Zuteilungsverfahren Direktzuteilung