Universität Wien

052320 VU Advanced Topics in Data Analysis (2017S)

6.00 ECTS (4.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Continuous assessment of course work
Moodle

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).

Details

max. 25 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Tuesday 07.03. 13:15 - 16:30 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Tuesday 14.03. 13:15 - 16:30 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Tuesday 21.03. 13:15 - 16:30 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Tuesday 28.03. 13:15 - 16:30 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Tuesday 04.04. 13:15 - 16:30 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Tuesday 25.04. 13:15 - 16:30 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Tuesday 02.05. 13:15 - 16:30 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Tuesday 09.05. 13:15 - 16:30 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Tuesday 16.05. 13:15 - 16:30 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Tuesday 23.05. 13:15 - 16:30 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Tuesday 30.05. 13:15 - 16:30 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Tuesday 13.06. 13:15 - 16:30 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Tuesday 20.06. 13:15 - 16:30 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Tuesday 27.06. 13:15 - 16:30 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG

Information

Aims, contents and method of the course

In this course will cover current topics in data mining reseach.
Goals: Participants are able to perform independent literature research, decide which techniques to apply to practical problems, perform a complete data mining process including all stages.
Contents: Current conference tutorials on "hot topics" of data mining research, e.g. on information-theoretic data mining, high-performance data mining, mining time series and graphs, research papers on these topics, participation in a data mining contest.
Methods: Lecture, student presentations, guided group work on practical challenges

Assessment and permitted materials

A maximum of 100 points can be achieved. Two practical tasks:
1) reviewing and presentation of a research paper, 40 points
2) participation in a data mining contest in small groups, 40 points
20 points for active participation in the lecture
Besides the material provided in the course and elaborated by the participants by doing literature research and group work, no further auxilliary material is allowed (also not required).

Minimum requirements and assessment criteria

To pass the course you need at least 50% of the points, attendence is mandatory.

Examination topics

Slides, research papers, independent literature study

Reading list

Lecture slides, research papers.

Association in the course directory

Module: AT-DA

Last modified: Mo 07.09.2020 15:30