052311 VU Data Mining (2023W)
Continuous assessment of course work
Labels
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).
- Registration is open from We 13.09.2023 09:00 to We 20.09.2023 09:00
- Deregistration possible until Sa 14.10.2023 23:59
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
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
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
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
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
Assessment and permitted materials
Active participation
Exercise sheets (individual work)
Programming assignments (group work)
Midterm and final exam (individual work)
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 examTo 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
30% Exercise sheets
30% Programming exercises in teams, peer-review
40% Midterm and final examTo 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
- 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, 2020Karsten 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 WorldAlbert-László Barabási, Network Science
Association in the course directory
Module: DM
Last modified: Th 30.11.2023 11:47
1. Graph kernels
2. Graph neural Networks
3. Centrality measures
4. Diffusion processes on graphs
5. Knowledge graphs and time evolving graphsSubject-specific goals:
- Understanding the characteristics of graph data
- Methods and techniques for mining and learning with graphs
- Analysis and interpretation of graph-structured scientific dataGeneric goals:
- Teamwork
- Improvement of programming skills
- Understanding of interplay in data mining, machine learning and other disciplines