Universität Wien

053611 VU Mathematics of Data Science (2020W)

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 first lecture will be on the 6th of October. For more information join the moodle page.

  • Tuesday 06.10. 09:45 - 12:00 Digital
  • Tuesday 13.10. 09:45 - 12:00 Digital
  • Tuesday 20.10. 09:45 - 12:00 Digital
  • Tuesday 27.10. 09:45 - 12:00 Digital
  • Tuesday 03.11. 09:45 - 12:00 Digital
  • Tuesday 10.11. 09:45 - 12:00 Digital
  • Tuesday 17.11. 09:45 - 12:00 Digital
  • Tuesday 24.11. 09:45 - 12:00 Digital
  • Tuesday 01.12. 09:45 - 12:00 Digital
  • Tuesday 15.12. 09:45 - 12:00 Digital
  • Tuesday 12.01. 09:45 - 12:00 Digital
  • Tuesday 19.01. 09:45 - 12:00 Digital
  • Tuesday 26.01. 09:45 - 12:00 Digital

Information

Aims, contents and method of the course

This will be held online. For more information please sign up to the associated moodle page. There be lectures every Tuesday from 09:45 to 12:00. The first 90 minutes thereof are in the format of a lecture. After that, we will take a break and then resume in an exercise class style format.

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 academical 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
* Principle components analysis
* Graphs and clustering
* Dimensionality reduction
* Sparsity and Compressed Sensing

Assessment and permitted materials

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

A. S. Bandeira, A. Singer, T. Strohmer, Mathematics of Data Science, https://people.math.ethz.ch/~abandeira/BandeiraSingerStrohmer-MDS-draft.pdf

Association in the course directory

Modul: MDS

Last modified: Fr 12.05.2023 00:13