040976 UK Classification, Clustering and Discrimination (2019S)
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 11.02.2019 09:00 to We 20.02.2019 12:00
- Deregistration possible until Th 14.03.2019 23:59
Details
max. 50 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
The lecture will start on 07.03.2019
Thursday
07.03.
16:45 - 18:15
Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
Thursday
14.03.
16:45 - 18:15
Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
Thursday
21.03.
16:45 - 18:15
Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
Thursday
28.03.
16:45 - 18:15
Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
Thursday
04.04.
16:45 - 18:15
Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
Thursday
11.04.
16:45 - 18:15
Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
Thursday
02.05.
16:45 - 18:15
Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
Thursday
09.05.
16:45 - 18:15
Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
Thursday
16.05.
16:45 - 18:15
Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
Thursday
23.05.
16:45 - 18:15
Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
Thursday
06.06.
16:45 - 18:15
Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
Thursday
13.06.
16:45 - 18:15
Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
Friday
21.06.
13:15 - 16:30
Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
Thursday
27.06.
16:45 - 20:00
Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
Information
Aims, contents and method of the course
The course presents basic and advanced 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. The topics of this course include neural networks, introduction to deep learning, support vector machines, feature selection, and main clustering algorithms.
Assessment and permitted materials
Exercises/projects will include the implementation of basic techniques and comparison/evaluation of discussed techniques on diverse real-life data sets.There will be one final exam.The final grade will be computed as follows:
0.5Exercises+0.5FinalExam
0.5Exercises+0.5FinalExam
Minimum requirements and assessment criteria
For a positive grade students must obtain at least
- 50% of points and in the final exam and
- 50% of points in exercises.
- 50% of points and in the final exam and
- 50% of points in exercises.
Examination topics
The whole material discussed in the class is relevant for the final exam.
Exercises will include the implementation of basic techniques and the comparison of discussed techniques on well known data sets in the literature.
Exercises will include the implementation of basic techniques and the comparison of discussed techniques on well known data sets in the literature.
Reading list
The literature will include different scientific papers and book chapters.
Association in the course directory
Last modified: Mo 07.09.2020 15:29