040976 UK Classification, Clustering and Discrimination (2018S)
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 14.02.2018 09:00 to We 21.02.2018 12:00
- Deregistration possible until We 14.03.2018 23:59
Details
max. 50 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
- Thursday 01.03. 16:45 - 18:15 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
- Friday 02.03. 09:45 - 11:15 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
- Thursday 15.03. 16:45 - 18:15 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
- Friday 16.03. 09:45 - 11:15 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
- Thursday 12.04. 16:45 - 18:15 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
- Friday 13.04. 09:45 - 11:15 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
- Thursday 26.04. 16:45 - 18:15 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
- Friday 27.04. 09:45 - 11:15 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
- Friday 11.05. 09:45 - 11:15 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
- Thursday 24.05. 16:45 - 18:15 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
- Friday 25.05. 09:45 - 11:15 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
- Thursday 07.06. 16:45 - 18:15 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
- Friday 08.06. 09:45 - 11:15 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
- Thursday 14.06. 16:45 - 18:15 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
- Friday 15.06. 09:45 - 11:15 Hörsaal 12 Oskar-Morgenstern-Platz 1 2.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.
There will be two evaluations: midterm and final exams.
The final grade will be computed as follows:
max{0.2Exercises+0.4Midterm+0.4Final, 0.5Midterm+0.5Final}
There will be two evaluations: midterm and final exams.
The final grade will be computed as follows:
max{0.2Exercises+0.4Midterm+0.4Final, 0.5Midterm+0.5Final}
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.
Examination topics
There will be two evaluations: midterm and final exams.
The final grade will be computed as follows:
max{0.2Exercises+0.4Midterm+0.4Final, 0.5Midterm+0.5Final}
The final grade will be computed as follows:
max{0.2Exercises+0.4Midterm+0.4Final, 0.5Midterm+0.5Final}
Reading list
Association in the course directory
Last modified: Mo 07.09.2020 15:29