Universität Wien
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250144 SE Seminar Mathematical Data Science (2022W)

4.00 ECTS (2.00 SWS), SPL 25 - Mathematik
Continuous assessment of course work
ON-SITE
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

First meeting on Oct 13

  • Thursday 06.10. 13:15 - 14:45 Seminarraum 18 Kolingasse 14-16, OG02
  • Thursday 13.10. 13:15 - 14:45 Seminarraum 18 Kolingasse 14-16, OG02
  • Thursday 20.10. 13:15 - 14:45 Seminarraum 18 Kolingasse 14-16, OG02
  • Thursday 27.10. 13:15 - 14:45 Seminarraum 18 Kolingasse 14-16, OG02
  • Thursday 03.11. 13:15 - 14:45 Seminarraum 18 Kolingasse 14-16, OG02
  • Thursday 10.11. 13:15 - 14:45 Seminarraum 18 Kolingasse 14-16, OG02
  • Thursday 17.11. 13:15 - 14:45 Seminarraum 18 Kolingasse 14-16, OG02
  • Thursday 24.11. 13:15 - 14:45 Seminarraum 18 Kolingasse 14-16, OG02
  • Thursday 01.12. 13:15 - 14:45 Seminarraum 18 Kolingasse 14-16, OG02
  • Thursday 15.12. 13:15 - 14:45 Seminarraum 18 Kolingasse 14-16, OG02
  • Thursday 12.01. 13:15 - 14:45 Seminarraum 18 Kolingasse 14-16, OG02
  • Thursday 19.01. 13:15 - 14:45 Seminarraum 18 Kolingasse 14-16, OG02
  • Thursday 26.01. 13:15 - 14:45 Seminarraum 18 Kolingasse 14-16, OG02
  • Monday 30.01. 08:00 - 20:00 Seminarraum 1 Porzellangasse 4, EG03
  • Tuesday 31.01. 08:00 - 14:45 Seminarraum 1 Porzellangasse 4, EG03

Information

Aims, contents and method of the course

In this seminar students familiarize themselves with current research on mathematical data science.

This will be done in groups of 3-5 students focusing on a particular topic such as

- Random Features
- Graph Neural Networks
- Equivariant Neural Networks
- Tractability of Neural Network training
- Physics Informed Neural Networks
- Fourier Operator Networks
- Nonnegative Matrix/Tensor Factorization
- Scattering Transforms
- Federated Learning

These topics will be investigated in depth. Each group is expected to conduct computational experiments, as well as present a summary talk related to its particular topic. These talks will take place in the end of the semester.

Assessment and permitted materials

Minimum requirements and assessment criteria

Examination topics

Reading list

Association in the course directory

MAMS

Last modified: Mo 23.01.2023 14:09