Universität Wien

052311 VU Data Mining (2023W)

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 03.10. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 05.10. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 10.10. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 12.10. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 17.10. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
    PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 19.10. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 24.10. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
    PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Tuesday 31.10. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Tuesday 07.11. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
  • Thursday 09.11. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 14.11. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
  • Thursday 16.11. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 21.11. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
  • Thursday 23.11. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 28.11. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
    Seminarraum 8, Währinger Straße 29 1.OG
  • Thursday 30.11. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 05.12. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
  • Thursday 07.12. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 12.12. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
  • Thursday 14.12. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 09.01. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
  • Thursday 11.01. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 16.01. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG
  • Thursday 18.01. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 23.01. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
    Hörsaal 3, Währinger Straße 29 3.OG
  • Thursday 25.01. 13:15 - 14:45 Seminarraum 6, Währinger Straße 29 1.OG
  • Tuesday 30.01. 15:00 - 16:30 Hörsaal 3, Währinger Straße 29 3.OG

Information

Aims, contents and method of the course

The lectures in this course will be given on-site and streamed via BigBlueButton. Exercise sessions will be on-site only and attending them is mandatory. The midterm and final exams will be on-site.

Important: Attendance in the first lecture on Tuesday 3.10. at 3 pm is mandatory. Due to the expected high number of participants, you can join online.

The lecture covers essential topics in data mining and machine learning with graphs and focuses on recent research on the following topics:
1. Graph kernels
2. Graph neural Networks
3. Centrality measures
4. Diffusion processes on graphs
5. Knowledge graphs and time evolving graphs

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

Generic goals:
- Teamwork
- 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)
Programming assignments (group work)
Midterm and 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:
30% Exercise sheets
30% Programming exercises in teams, peer-review
40% Midterm and final exam

To successfully complete the course, you must achieve at least 40% of the points in the midterm and at least 40% of the points in the 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

- Kernel methods
- Graph kernels
- Graph neural networks
- Centrality measures
- Diffusion processes on graphs
- Network evolution
- Knowledge graphs

Reading list

Ian Goodfellow, Yoshua Bengio, Aaron Courville: Deep Learning, MIT Press, 2017.

Shai Shalev-Shwartz, Shai Ben-David: Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press 2014.

Nils M. Kriege, Fredrik D. Johansson, Christopher Morris: A Survey on Graph Kernels, Applied Network Science, Machine learning with graphs, 5:6, 2020

Karsten M. Borgwardt, M. Elisabetta Ghisu, Felipe Llinares-López, Leslie O'Bray, Bastian Rieck: Graph Kernels: State-of-the-Art and Future Challenges. Found. Trends Mach. Learn. 13(5-6) (2020)

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 30.11.2023 11:47