Universität Wien

050035 VU Machine Learning (2016S)

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

URL http://vda.univie.ac.at/Teaching/ML/16s
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: English

Lecturers

Classes (iCal) - next class is marked with N

  • Friday 04.03. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
  • Friday 18.03. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
  • Friday 08.04. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
  • Friday 15.04. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
  • Friday 22.04. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
  • Friday 29.04. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
  • Friday 06.05. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
  • Friday 13.05. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
  • Friday 20.05. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
  • Friday 27.05. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
  • Friday 03.06. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
  • Friday 10.06. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
  • Friday 17.06. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
  • Friday 24.06. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.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 of practical exercises (50%), course feedback (10%), and a final test (40%)

Minimum requirements and assessment criteria

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

Examination topics

Lectures with parctical exercises, mainly by using Matlab, R or Python.

Reading list

Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer 2007;
Hastie-Tibshirani-Friedman: The Elements of Statistical Learning, Springer 2009

Association in the course directory

Last modified: Mo 07.09.2020 15:29