Universität Wien
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052311 VU Data Mining (2025W)

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

  • Thursday 02.10. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 07.10. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 09.10. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 14.10. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 16.10. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 21.10. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 23.10. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 28.10. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 30.10. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 04.11. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 06.11. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 11.11. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
    Seminarraum 7, Kolingasse 14-16, OG01
  • Thursday 13.11. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 18.11. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 20.11. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 25.11. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 27.11. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 02.12. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 04.12. 13:15 - 14:45 Hörsaal 2, Währinger Straße 29 2.OG
  • Tuesday 09.12. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 11.12. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 16.12. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 18.12. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Thursday 08.01. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 13.01. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 15.01. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 20.01. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 22.01. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 27.01. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 29.01. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG

Information

Aims, contents and method of the course

This course will be held on-site. The attendance in exercise sessions is mandatory.

Important: Attendance in the first lecture is mandatory.

The course covers essential topics in data mining and machine learning and focuses on recent research on the following topics:
1. Density-based clustering, high-dimensional clustering, alternative and deep clustering
2. Causality
3. Deep learning on sets and structured data

Subject-specific goals:
- Understanding the characteristics of complex data
- Methods and techniques for data mining and machine learning

Generic goals:
- Improvement of programming skills
- Understanding of interplay in data mining, machine learning and other disciplines

Assessment and permitted materials

Active participation
Exercises (individual work)
Exams (individual work)

Minimum requirements and assessment criteria

A mandatory prerequisite for this class is the successful completion of FDA (052300 VU Foundations of Data Analysis) or an equivalent lecture. Experience in programming in Python is expected.

Components:
40% Exercises (3 exercise sheets in total)
60% Exams (3 written exams in total)

Grading:
>87,00 %: 1
between 75,00 % and 86,99 %: 2
between 63,00 % and 74,99 %: 3
between 50,00 % and 62,99 %: 4
< 50%: 5

Examination topics

- Density-based clustering
- High-dimensional clustering
- Alternative clustering
- Deep clustering
- Causality
- Neural networks
- Universality, in- and equivariance
- Deep learning on sets
- Deep learning on graphs

Reading list

Association in the course directory

Module: DM

Last modified: Th 13.11.2025 13:26