Universität Wien

052311 VU Data Mining (2024W)

6.00 ECTS (4.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Continuous assessment of course work
Moodle
Tu 12.11. 15:00-16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02

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 01.10. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 03.10. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 08.10. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 10.10. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 15.10. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 17.10. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 22.10. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 24.10. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 29.10. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 31.10. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 05.11. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 07.11. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Thursday 14.11. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 19.11. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 21.11. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 26.11. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 28.11. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 03.12. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 05.12. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 10.12. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 12.12. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 17.12. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Tuesday 07.01. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 09.01. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 14.01. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 16.01. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 21.01. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 23.01. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 28.01. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 30.01. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG

Information

Aims, contents and method of the course

The lectures in this course will be given on-site.
There will be exercise sessions and attending them is mandatory.
The final exams will be on-site.

Important: Attendance in the first lecture on Tuesday 1.10. at 3 pm is mandatory.

The lecture covers essential topics in data mining and machine learning and focuses on recent research on the following topics:
1. Density-based clustering
2. High-dimensional clustering, alternative and deep clustering.
3. Causality
4. Deep learning on sets and graphs
5. Diffusion processes on graphs and graph mining

Subject-specific goals:
- Understanding the characteristics of complex data
- Methods and techniques for data mining and machine learning
- Analysis and interpretation of graph-structured scientific data

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

Assessment and permitted materials

Active participation
Exercise sheets (individual work)
Final exam (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:
10% Each exercise sheet (5 in total)
50% Final exam

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
- Deep learning on sets
- Deep learning on graphs
- Diffusion processes on graphs
- Information access
- Polarization

Reading list

David Easley and Jon Kleinberg, Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Albert-László Barabási, Network Science

Association in the course directory

Module: DM

Last modified: Th 19.09.2024 10:25