040649 UK Machine Learning (2013W)
Continuous assessment of course work
Labels
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).
- Registration is open from Fr 06.09.2013 09:00 to Fr 20.09.2013 14:00
- Registration is open from We 25.09.2013 09:00 to Th 26.09.2013 17:00
- Deregistration possible until Mo 14.10.2013 23:59
Details
max. 50 participants
Language: German
Lecturers
Classes (iCal) - next class is marked with N
- Monday 07.10. 18:00 - 20:00 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
- Monday 14.10. 18:00 - 20:00 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
- Monday 21.10. 18:00 - 20:00 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
- Monday 28.10. 18:00 - 20:00 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
- Monday 04.11. 18:00 - 20:00 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
- Monday 11.11. 18:00 - 20:00 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
- Monday 18.11. 18:00 - 20:00 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
- Monday 25.11. 18:00 - 20:00 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
- Monday 02.12. 18:00 - 20:00 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
- Monday 09.12. 18:00 - 20:00 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
- Monday 16.12. 18:00 - 20:00 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
- Monday 13.01. 18:00 - 20:00 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
- Monday 20.01. 18:00 - 20:00 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
- Friday 24.01. 17:00 - 18:30 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
- Monday 27.01. 18:00 - 20:00 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
- Monday 24.02. 17:00 - 19:00 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
Information
Aims, contents and method of the course
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. Techniques to be covered include:- decision trees- learning rules- neural networks- Bayesian classification- support vector machines- association rules.
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
(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
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].