040649 UK Machine Learning (2016W)
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 Mo 12.09.2016 09:00 to Th 22.09.2016 14:00
- Deregistration possible until Fr 14.10.2016 14:00
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
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
- 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
(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].