Universität Wien

050035 VU Machine Learning (2013S)

3.00 ECTS (2.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Continuous assessment of course work
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 25 participants
Language: German

Lecturers

Classes (iCal) - next class is marked with N

  • Friday 01.03. 16:45 - 18:15 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Friday 08.03. 16:45 - 18:15 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Friday 15.03. 16:45 - 18:15 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Friday 22.03. 16:45 - 18:15 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Friday 12.04. 16:45 - 18:15 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Friday 19.04. 16:45 - 18:15 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Friday 26.04. 16:45 - 18:15 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Friday 03.05. 16:45 - 18:15 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Friday 10.05. 16:45 - 18:15 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Friday 17.05. 16:45 - 18:15 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Friday 24.05. 16:45 - 18:15 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Friday 31.05. 16:45 - 18:15 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Friday 07.06. 16:45 - 18:15 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Friday 14.06. 16:45 - 18:15 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Friday 21.06. 16:45 - 18:15 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Friday 28.06. 16:45 - 18:15 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG

Information

Aims, contents and method of the course

Basic methods in machine learning: Supervised Learning (classification): Naive Bayes, Classification Trees, Combination Methods, Support Vector Machine, Neural Networks, Genetic Algorithms; Unsupervised Learning (Cluster analysis): K-Means, SOM, Isomap, Model based Clustering

Assessment and permitted materials

Attandence of lectures, solving and solving of practical exercises (60%), final test (40%)

Minimum requirements and assessment criteria

getting familiar with basic ideas in machine learning and application of the methods wit R and Python.

Examination topics

Lectures with parctical exercises, mainly by using R and Python.

Reading list

Stephen Marsland: Machine Learning, An Algorithmic Perspective,Chapman & Hall/CRC. 2009.
X. Wu, V. Kumar: The Top Ten Algorithms in Data Mining, Chapman&Hall/CRC Data Mining and Knowledge Discovery Series, 2009
Hastie-Tibshirani-Friedman: The Elements of Statistical Learning, Springer 2009
Cherkassky-Mulier: Learning from Data, IEEE Press, Wiley 2007

Association in the course directory

Last modified: Mo 07.09.2020 15:29