052311 VU Data Mining (2020W)
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 Mo 14.09.2020 09:00 to Mo 21.09.2020 09:00
- Deregistration possible until We 14.10.2020 23:59
Details
max. 25 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
Thursday
01.10.
08:00 - 09:30
Seminarraum 6, Währinger Straße 29 1.OG
Tuesday
06.10.
15:00 - 16:30
Seminarraum 4, Währinger Straße 29 1.UG
Thursday
08.10.
08:00 - 09:30
Seminarraum 6, Währinger Straße 29 1.OG
Tuesday
13.10.
15:00 - 16:30
Seminarraum 4, Währinger Straße 29 1.UG
Thursday
15.10.
08:00 - 09:30
Seminarraum 6, Währinger Straße 29 1.OG
Tuesday
20.10.
15:00 - 16:30
Seminarraum 4, Währinger Straße 29 1.UG
Thursday
22.10.
08:00 - 09:30
Seminarraum 6, Währinger Straße 29 1.OG
Tuesday
27.10.
15:00 - 16:30
Seminarraum 4, Währinger Straße 29 1.UG
Thursday
29.10.
08:00 - 09:30
Seminarraum 6, Währinger Straße 29 1.OG
Tuesday
03.11.
15:00 - 16:30
Seminarraum 4, Währinger Straße 29 1.UG
Thursday
05.11.
08:00 - 09:30
Seminarraum 6, Währinger Straße 29 1.OG
Tuesday
10.11.
15:00 - 16:30
Seminarraum 4, Währinger Straße 29 1.UG
Thursday
12.11.
08:00 - 09:30
Seminarraum 6, Währinger Straße 29 1.OG
Tuesday
17.11.
15:00 - 16:30
Seminarraum 4, Währinger Straße 29 1.UG
Thursday
19.11.
08:00 - 09:30
Seminarraum 6, Währinger Straße 29 1.OG
Tuesday
24.11.
15:00 - 16:30
Seminarraum 4, Währinger Straße 29 1.UG
Thursday
26.11.
08:00 - 09:30
Seminarraum 6, Währinger Straße 29 1.OG
Tuesday
01.12.
15:00 - 16:30
Seminarraum 4, Währinger Straße 29 1.UG
Thursday
03.12.
08:00 - 09:30
Seminarraum 6, Währinger Straße 29 1.OG
Thursday
10.12.
08:00 - 09:30
Seminarraum 6, Währinger Straße 29 1.OG
Tuesday
15.12.
15:00 - 16:30
Seminarraum 4, Währinger Straße 29 1.UG
Thursday
17.12.
08:00 - 09:30
Seminarraum 6, Währinger Straße 29 1.OG
Thursday
07.01.
08:00 - 09:30
Seminarraum 6, Währinger Straße 29 1.OG
Tuesday
12.01.
15:00 - 16:30
Seminarraum 4, Währinger Straße 29 1.UG
Thursday
14.01.
08:00 - 09:30
Seminarraum 6, Währinger Straße 29 1.OG
Tuesday
19.01.
15:00 - 16:30
Seminarraum 4, Währinger Straße 29 1.UG
Thursday
21.01.
08:00 - 09:30
Seminarraum 6, Währinger Straße 29 1.OG
Tuesday
26.01.
15:00 - 16:30
Seminarraum 4, Währinger Straße 29 1.UG
Thursday
28.01.
08:00 - 09:30
Seminarraum 6, Währinger Straße 29 1.OG
Information
Aims, contents and method of the course
Assessment and permitted materials
Active participation
Work on exercise-sheets
Work on programming assignments in groups
Peer-review of other participants
Final exam
Work on exercise-sheets
Work on programming assignments in groups
Peer-review of other participants
Final exam
Minimum requirements and assessment criteria
Mandatory prerequisite for this class is the successful completion of FDA or an equivalent lecture.
FDA: 052300 VU Foundations of Data Analysis40% Exercise-sheets
30% Programming exercises in Team, peer-review
30% Final exam>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
FDA: 052300 VU Foundations of Data Analysis40% Exercise-sheets
30% Programming exercises in Team, peer-review
30% Final exam>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)
- Feature selection
- Submodular Functions
- Granger Causality
- Causal Inference by Transfer Entropy
- Causal Inference by Compression Schemes
- Clustering of High Dimensional Data (Subspace Clustering, Deep Clustering)
- Kernel Methods
- Mining and learning with graphs (Graph Kernels, Graph Neural Networks)
- Feature selection
- Submodular Functions
- Granger Causality
- Causal Inference by Transfer Entropy
- Causal Inference by Compression Schemes
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.
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.
Association in the course directory
Module: DM
Last modified: Mo 28.09.2020 15:28
The lecture covers essential topics in Data Mining and Knowledge Discovery Databases (KDD) and focuses on recent research topics in clustering, graph mining, submodularity and causal inference.
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
- Extraction of causal patterns from the data
Generic goals:
- Teamwork
- Improvement of programming skills
- Understanding of interplay in Data Mining and other disciplinesImportant: We will hold the first lecture online on Thursday 01.10. at 08:00 o'clock. You should not come to the class room.