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052311 VU Data Mining (2020W)

Continuous assessment of course work

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

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

This course will be hybrid, we will give the lecture in a physical classroom (as long as possible), but will record each lecture and make them accessible online via Moodle.
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 disciplines

Important: We will hold the first lecture online on Thursday 01.10. at 08:00 o'clock. You should not come to the class room.

Assessment and permitted materials

Active participation
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 Analysis

40% 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

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.

Association in the course directory

Module: DM

Last modified: Mo 28.09.2020 15:28