Universität Wien

052311 VU Data Mining (2021W)

6.00 ECTS (4.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Continuous assessment of course work
MIXED
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 05.10. 15:00 - 16:30 Digital
  • Thursday 07.10. 08:00 - 09:30 Digital
  • Tuesday 12.10. 15:00 - 16:30 Digital
  • Thursday 14.10. 08:00 - 09:30 Digital
  • Tuesday 19.10. 15:00 - 16:30 Digital
  • Thursday 21.10. 08:00 - 09:30 Digital
  • Thursday 28.10. 08:00 - 09:30 Digital
    Hörsaal 42 Hauptgebäude, 2.Stock, Stiege 7
  • Thursday 04.11. 08:00 - 09:30 Digital
  • Tuesday 09.11. 15:00 - 16:30 Digital
  • Thursday 11.11. 08:00 - 09:30 Digital
  • Tuesday 16.11. 15:00 - 16:30 Digital
  • Thursday 18.11. 08:00 - 09:30 Digital
  • Tuesday 23.11. 15:00 - 16:30 Digital
    Hörsaal 5 ZfT Philippovichgasse 11, 1.OG
  • Thursday 25.11. 08:00 - 09:30 Digital
  • Tuesday 30.11. 15:00 - 16:30 Digital
  • Thursday 02.12. 08:00 - 09:30 Digital
    Hörsaal 42 Hauptgebäude, 2.Stock, Stiege 7
  • Tuesday 07.12. 15:00 - 16:30 Digital
  • Thursday 09.12. 08:00 - 09:30 Digital
  • Tuesday 14.12. 15:00 - 16:30 Digital
  • Thursday 16.12. 08:00 - 09:30 Digital
  • Tuesday 11.01. 15:00 - 16:30 Digital
  • Thursday 13.01. 08:00 - 09:30 Digital
  • Tuesday 18.01. 15:00 - 16:30 Digital
  • Thursday 20.01. 08:00 - 09:30 Digital
  • Tuesday 25.01. 15:00 - 16:30 Digital
  • Thursday 27.01. 08:00 - 09:30 Digital
    Hörsaal 42 Hauptgebäude, 2.Stock, Stiege 7

Information

Aims, contents and method of the course

This course will be taught in a hybrid format. The lectures will be given online via Big Blue Button, recorded and made available in Moodle. We plan to have Q&A (Questions-and-Answers) sessions regarding the programming assignments and the two exams on-site if the situation allows.
Important: We will hold the first lecture online on Tuesday 05.10. at 15:00 o'clock. Big Blue Button Link for the first lecture: https://moodle.univie.ac.at/mod/bigbluebuttonbn/guestlink.php?gid=zJv4ph9GNUEt

The lecture covers essential topics in Data Mining and Machine Learning and focuses on recent research on the following topics:
1. Clustering
2. Natural language processing
3. Learning with graph-structured data
4. Multi-view learning

Subject-specific goals:
- Analysis and interpretation of scientific data
- Evaluate results of the analysis process
- Implementation of scalable solutions for huge amounts of data
- 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:
40% Exercise sheets
30% Programming exercises in teams, peer-review
30% Midterm and final exam

To successfully complete the course you need to achieve at least 30% of the points in each of the parts.

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)
- Sequence-to-Sequence learning
- Text mining
- Sentiment analysis
- Kernel methods
- Mining and learning with graphs (graph kernels, graph neural networks)
- Data streams
- Granger Causality (updated, 17. Dezember 2021)

And eventually other topics such as:
- Multi-view, multi-instance learning
- Gradient boosting trees

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)

Association in the course directory

Module: DM

Last modified: Fr 12.05.2023 00:13