Universität Wien
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053611 VU Mathematics of Data Science (2023W)

4.00 ECTS (3.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Continuous assessment of course work
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

The associated moodle page will contain more material about the lecture.

  • Tuesday 03.10. 09:45 - 12:00 Seminarraum 18 Kolingasse 14-16, OG02
  • Tuesday 10.10. 09:45 - 12:00 Seminarraum 18 Kolingasse 14-16, OG02
  • Tuesday 17.10. 09:45 - 12:00 Seminarraum 18 Kolingasse 14-16, OG02
  • Tuesday 24.10. 09:45 - 12:00 Seminarraum 18 Kolingasse 14-16, OG02
  • Tuesday 31.10. 09:45 - 12:00 Seminarraum 18 Kolingasse 14-16, OG02
  • Tuesday 07.11. 09:45 - 12:00 Seminarraum 18 Kolingasse 14-16, OG02
  • Tuesday 14.11. 09:45 - 12:00 Seminarraum 18 Kolingasse 14-16, OG02
  • Tuesday 21.11. 09:45 - 12:00 Seminarraum 18 Kolingasse 14-16, OG02
  • Tuesday 28.11. 09:45 - 12:00 Seminarraum 18 Kolingasse 14-16, OG02
  • Tuesday 05.12. 09:45 - 12:00 Seminarraum 18 Kolingasse 14-16, OG02
  • Tuesday 12.12. 09:45 - 12:00 Seminarraum 18 Kolingasse 14-16, OG02
  • Tuesday 09.01. 09:45 - 12:00 Seminarraum 18 Kolingasse 14-16, OG02
  • Tuesday 16.01. 09:45 - 12:00 Seminarraum 18 Kolingasse 14-16, OG02
  • Tuesday 23.01. 09:45 - 12:00 Seminarraum 18 Kolingasse 14-16, OG02
  • Tuesday 30.01. 09:45 - 12:00 Seminarraum 18 Kolingasse 14-16, OG02

Information

Aims, contents and method of the course

This course establishes a mathematical basis required to understand tools and methods in data science. Since it is expected that the students in this course come from a broad range of academic backgrounds, the classes will be adapted to the prior knowledge of the students.

In this course we will get to know the following topics in various degrees of depth: high-dimensionality and dimension reduction, principle components analysis, graphs and clustering, image and signal processing, Fourier analysis, sparsity and compressed sensing.

Assessment and permitted materials

Written or oral exam at the end of the semester.

Minimum requirements and assessment criteria

Basic knowledge of all mathematical concepts presented in the lecture.

Examination topics

Everything covered in the lectures.

Reading list

- Bishop: Pattern Recognition and Machine Learning
- Bandeira, Singer, Strohmer: Mathematics of Data Science, https://people.math.ethz.ch/~abandeira/BandeiraSingerStrohmer-MDS-draft.pdf
- Brunton, Kutz: Data-Driven Science and Engineering
- Shalev-Shwartz, Ben-David: Understanding Machine Learning

Association in the course directory

Modul: MDS

Last modified: Mo 02.10.2023 16:47