Universität Wien

052311 VU Data Mining (2022W)

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 04.10. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 06.10. 08:00 - 09:30 Digital
  • Tuesday 11.10. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 13.10. 08:00 - 09:30 Digital
  • Tuesday 18.10. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 20.10. 08:00 - 09:30 Digital
  • Tuesday 25.10. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 27.10. 08:00 - 09:30 Digital
  • Thursday 03.11. 08:00 - 09:30 Digital
  • Tuesday 08.11. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 10.11. 08:00 - 09:30 Digital
  • Tuesday 15.11. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 17.11. 08:00 - 09:30 Digital
  • Tuesday 22.11. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 24.11. 08:00 - 09:30 Digital
  • Tuesday 29.11. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 01.12. 08:00 - 09:30 Digital
  • Tuesday 06.12. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Tuesday 13.12. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 15.12. 08:00 - 09:30 Digital
  • Tuesday 10.01. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 12.01. 08:00 - 09:30 Digital
  • Tuesday 17.01. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 19.01. 08:00 - 09:30 Digital
  • Tuesday 24.01. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Thursday 26.01. 08:00 - 09:30 Digital
  • Tuesday 31.01. 15:00 - 16:30 PC-Seminarraum 3, Kolingasse 14-16, OG02

Information

Aims, contents and method of the course

This course will be taught in a hybrid format. The lectures on Tuesday will be held on site and the lectures on Thursday will be held online via Big Blue Button. The online lectures will be recorded and made available in Moodle. The midterm and final exam will be onsite.
Important: We will hold the first lecture onsite on Tuesday 04.10. at 15:00 o'clock. If you cannot join onsite, you can use the following link to join: https://moodle.univie.ac.at/mod/bigbluebuttonbn/guestlink.php?gid=2LCNMyla8QYD

The lecture covers essential topics in Data Mining and Machine Learning and focuses on recent research on the following topics:
1. Clustering
2. Learning with graph-structured data
3. Community structure in graphs
4. Diffusion processes on graphs

Subject-specific goals:
- Analysis and interpretation of scientific data
- Evaluate results of the analysis process
- Users support and advice

Generic goals:
- Teamwork
- Improvement of programming skills
- Understanding of interplay in Data Mining and other disciplines

Assessment and permitted materials

Active participation
Exercise sheets (individual work)
Programming assignments (group work)
Peer-review of other participants (individual 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 complete the course, you need to achieve at least 30% of the overall points in the exercises and at least 30% of the points for each exam and programming exercise.

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

- Dimensionality reduction
- Clustering of high dimensional data (subspace clustering, deep clustering)
- Kernel methods
- Mining and learning with graphs (graph kernels, graph neural networks)
- Community structure in graphs (networks)
- Knowledge graphs
- Multi-relational graphs
- Diffusion processes on graphs

Reading list

Han J., Kamber M., Pei J. Data Mining: Concepts and Techniques
Tan P.-N., Steinbach M., Kumar V. Introduction to Data Mining
Ester M., Sander J. Knowledge Discovery in Databases: Techniken und Anwendungen
Goodfellow, Ian, et al. Deep Learning
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 11.05.2023 11:27