040976 UK Classification, Clustering and Discrimination (2017S)
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 We 15.02.2017 09:00 to We 22.02.2017 12:00
- Deregistration possible until Tu 14.03.2017 23:59
Details
max. 50 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
- Thursday 02.03. 11:30 - 13:00 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Thursday 09.03. 11:30 - 13:00 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Thursday 16.03. 11:30 - 13:00 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Thursday 23.03. 11:30 - 13:00 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Thursday 30.03. 11:30 - 13:00 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Thursday 06.04. 11:30 - 13:00 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Thursday 27.04. 11:30 - 13:00 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Thursday 04.05. 11:30 - 13:00 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Thursday 11.05. 11:30 - 13:00 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Thursday 18.05. 11:30 - 13:00 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Thursday 01.06. 11:30 - 13:00 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Thursday 08.06. 11:30 - 13:00 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Thursday 22.06. 11:30 - 13:00 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
- Thursday 29.06. 11:30 - 13:00 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock
Information
Aims, contents and method of the course
The course presents basic methods used in the areas of classification, clustering and discrimination. Rather than on classical statistical procedures, the focus is on modern techniques of machine learning which also enable applications to “big data” and business analytics. In particular, supervised and unsupervised learning algorithms are discussed, decision tree techniques are introduced, neural network methodology is outlined, and diverse clustering algorithms are presented.
Assessment and permitted materials
The participants get exercises, some of which are to be solved theoretically, while others require programming or application of software. The latter type of exercises can also be handled by groups of participants. The solutions to the exercises are presented during the course.
Minimum requirements and assessment criteria
The presentation of the solution to a standard theoretical exercise yields one point; the number of points obtained from exercises requiring programming is to be agreed upon from case to case. For completing the course with a positive grade, three points are the minimum.
Examination topics
No special exam.
Reading list
Trevor Hastie, Robert Tibshirani, Jerome Friedman, ``The Elements of Statistical Learning: Data Mining, Inference, and Prediction'', SpringerEthem Alpaydin, ``Introduction to Machine Learning'', MIT PressChristopher M. Bishop, ``Pattern Recognition and Machine Learning'', Springer
Association in the course directory
Last modified: Mo 07.09.2020 15:29