Inhalt

[ 875BIN2MUTU13 ] UE (*)Machine Learning: Unsupervised Techniques

Versionsauswahl
Es ist eine neuere Version 2016W dieser LV im Curriculum Masterstudium Elektronik und Informationstechnik 2018W vorhanden.
(*) 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 2015W
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 (*)in collaboration with 675MLDAMSTU13: UE Machine Learning: Supervised Techniques (1,5 ECTS) equivalent to
875BIN2TMLU12: UE Theoretical Bioinformatics and Machine Learning (3 ECTS) -or- BIMPHUEBIN2: UE Bioinformatik II: Theoretische Bioinformatik und Maschinelles Lernen (3 ECTS)
Präsenzlehrveranstaltung
Teilungsziffer 35
Zuteilungsverfahren Direktzuteilung