[ 875BIN2MUTV13 ] VL Machine Learning: Unsupervised Techniques

Es ist eine neuere Version 2016W dieser LV im Curriculum Masterstudium Computer Science 2016W vorhanden.
Workload Ausbildungslevel Studienfachbereich VerantwortlicheR Semesterstunden Anbietende Uni
3 ECTS M1 - Master 1. Jahr Informatik Sepp Hochreiter 2 SSt Johannes Kepler Universität Linz
Quellcurriculum Masterstudium Bioinformatics 2013W
Ziele Machine learning is concerned inferring models/relationships by learning from data. Machine learning methods are gaining importance in various fields, such as, process modeling, speech and image processing, and so forth. In recent years, bioinformatics has become one of the most prominent application areas of machine learning methods: The massive data amounts produced by recent and currently emerging high-throughput biotechnologies provide unprecedented potentials, but also pose yet unseen computational challenges in the analysis of biological data.

This course focuses on so-called unsupervised machine learning techniques, that is, methods aiming at inferring structure/models in data without an explicit target. The students should aquire skills to choose, use, and adapt methods for clustering, data projection, and data reduction for tasks in science and engineering. The students should particularly understand the underlying mathematical objectives and principles of unsupervised machine learning methods.

  • 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
Beurteilungskriterien Exam (written or oral)
Lehrmethoden Slide presentations complemented by examples presented on the blackboard
Abhaltungssprache Englisch
Literatur Electronic course material is made available for download
Lehrinhalte wechselnd? Nein
Äquivalenzen in collaboration with 675MLDAMSTV13: VL Machine Learning: Supervised Techniques (3 ECTS) equivalent to
875BIN2TMLV12: VL Theoretical Bioinformatics and Machine Learning (6 ECTS) -or- BIMPHVOBIN2: VO Bioinformatik II: Theoretische Bioinformatik und Maschinelles Lernen (6 ECTS)
Teilungsziffer -
Zuteilungsverfahren Direktzuteilung