Universität Wien

040649 UK Machine Learning (2016W)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
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. 30 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Wednesday 05.10. 16:45 - 18:15 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 12.10. 16:45 - 18:15 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 19.10. 16:45 - 18:15 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 09.11. 16:45 - 18:15 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 16.11. 16:45 - 18:15 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 23.11. 16:45 - 18:15 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 30.11. 16:45 - 18:15 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 07.12. 16:45 - 18:15 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 14.12. 16:45 - 18:15 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 11.01. 16:45 - 18:15 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 18.01. 16:45 - 18:15 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 25.01. 16:45 - 18:15 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 03.03. 16:45 - 19:00 Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß

Information

Aims, contents and method of the course

This course is about methods and algorithms for an automatic search
for patterns in electronically stored data. Useful patterns allows
us to make non-trivial predictions of new data. Try to derive your
own understanding of machine learning by using the following
definition: "Things learn when they change they behavior that makes
them perform better in the future" [1].

Assessment and permitted materials

Minimum requirements and assessment criteria

The course will introduce practical machine learning tools and techniques with applications to data mining using commercial, scientific, and web data sets. Topics to be covered include:
- Data preparation
- Evaluation, Model Selection
- Instance Based Learning
- Decision Trees
- Random Forests
- Naive Bayes
- Bayesian Networks
- Ensemble learning

Examination topics

Reading list

[1] Data Mining: Practical Machine Learning Tools and Techniques
(Third Edition, 2011), Ian H. Witten, Eibe Frank, Mark A. Hall
http://www.cs.waikato.ac.nz/~ml/weka/book.html

[2] Introduction to Machine Learning
Ethem Alpaydin
The MIT Press, October 2004, ISBN 0-262-01211-1

Association in the course directory

Last modified: Mo 07.09.2020 15:29