Universität Wien

052311 VU Data Mining (2022W)

6.00 ECTS (4.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Prüfungsimmanente Lehrveranstaltung
Moodle

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 25 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

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

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

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

Art der Leistungskontrolle und erlaubte Hilfsmittel

Active participation
Exercise sheets (individual work)
Programming assignments (group work)
Peer-review of other participants (individual work)
Midterm and final exam (individual work)

Mindestanforderungen und Beurteilungsmaßstab

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

Prüfungsstoff

- 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

Literatur

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

Zuordnung im Vorlesungsverzeichnis

Module: DM

Letzte Änderung: Do 11.05.2023 11:27