Universität Wien

050035 VU Machine Learning (2014S)

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

URL http://vda.univie.ac.at/Teaching/ML/14s
CEWebs

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 07.03. 15:00 - 16:30 PC-Unterrichtsraum 6, Währinger Straße 29 2.OG
  • Friday 14.03. 15:00 - 16:30 PC-Unterrichtsraum 6, Währinger Straße 29 2.OG
  • Friday 21.03. 15:00 - 16:30 PC-Unterrichtsraum 6, Währinger Straße 29 2.OG
  • Friday 28.03. 15:00 - 16:30 PC-Unterrichtsraum 6, Währinger Straße 29 2.OG
  • Friday 04.04. 15:00 - 16:30 PC-Unterrichtsraum 6, Währinger Straße 29 2.OG
  • Friday 11.04. 15:00 - 16:30 PC-Unterrichtsraum 6, Währinger Straße 29 2.OG
  • Friday 02.05. 15:00 - 16:30 PC-Unterrichtsraum 6, Währinger Straße 29 2.OG
  • Friday 09.05. 15:00 - 16:30 PC-Unterrichtsraum 6, Währinger Straße 29 2.OG
  • Friday 16.05. 15:00 - 16:30 PC-Unterrichtsraum 6, Währinger Straße 29 2.OG
  • Friday 23.05. 15:00 - 16:30 PC-Unterrichtsraum 6, Währinger Straße 29 2.OG
  • Friday 30.05. 15:00 - 16:30 PC-Unterrichtsraum 6, Währinger Straße 29 2.OG
  • Friday 06.06. 15:00 - 16:30 PC-Unterrichtsraum 6, Währinger Straße 29 2.OG
  • Friday 13.06. 15:00 - 16:30 PC-Unterrichtsraum 6, Währinger Straße 29 2.OG
  • Friday 20.06. 15:00 - 16:30 PC-Unterrichtsraum 6, Währinger Straße 29 2.OG
  • Friday 27.06. 15:00 - 16:30 PC-Unterrichtsraum 6, Währinger Straße 29 2.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