040649 UK Machine Learning (2011W)
Continuous assessment of course work
Labels
The course will be given in English.
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 Mo 05.09.2011 09:00 to We 21.09.2011 17:00
- Registration is open from Tu 27.09.2011 09:00 to Fr 07.10.2011 17:00
- Deregistration possible until Fr 14.10.2011 23:59
Details
max. 50 participants
Language: German
Lecturers
Classes (iCal) - next class is marked with N
Monday
17.10.
18:00 - 20:00
Leopold-Schmetterer-Seminarraum, Universitätsstraße 5, 3.Stock
Monday
24.10.
18:00 - 20:00
Leopold-Schmetterer-Seminarraum, Universitätsstraße 5, 3.Stock
Monday
31.10.
18:00 - 20:00
Leopold-Schmetterer-Seminarraum, Universitätsstraße 5, 3.Stock
Monday
07.11.
18:00 - 20:00
Leopold-Schmetterer-Seminarraum, Universitätsstraße 5, 3.Stock
Monday
14.11.
18:00 - 20:00
Leopold-Schmetterer-Seminarraum, Universitätsstraße 5, 3.Stock
Monday
21.11.
18:00 - 20:00
Leopold-Schmetterer-Seminarraum, Universitätsstraße 5, 3.Stock
Monday
28.11.
18:00 - 20:00
Leopold-Schmetterer-Seminarraum, Universitätsstraße 5, 3.Stock
Monday
05.12.
18:00 - 20:00
Leopold-Schmetterer-Seminarraum, Universitätsstraße 5, 3.Stock
Monday
12.12.
18:00 - 20:00
Leopold-Schmetterer-Seminarraum, Universitätsstraße 5, 3.Stock
Monday
09.01.
18:00 - 20:00
Leopold-Schmetterer-Seminarraum, Universitätsstraße 5, 3.Stock
Monday
16.01.
18:00 - 20:00
Leopold-Schmetterer-Seminarraum, Universitätsstraße 5, 3.Stock
Monday
23.01.
18:00 - 20:00
Leopold-Schmetterer-Seminarraum, Universitätsstraße 5, 3.Stock
Monday
30.01.
18:00 - 20:00
Leopold-Schmetterer-Seminarraum, Universitätsstraße 5, 3.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].