Universität Wien
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280522 VU Data Science in Astrophysics (2024S)

8.00 ECTS (4.00 SWS), SPL 28 - Erdwissenschaften, Meteorologie - Geophysik und
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. 55 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Introduction on 04.03.2024 11:30 - 13:00 Littrow-Hörsaal Astronomie Sternwarte

  • Monday 04.03. 11:30 - 13:00 Littrow-Hörsaal Astronomie Sternwarte, Türkenschanzstraße 17
  • Thursday 07.03. 09:45 - 11:15 Littrow-Hörsaal Astronomie Sternwarte, Türkenschanzstraße 17
  • Monday 11.03. 11:30 - 13:00 Littrow-Hörsaal Astronomie Sternwarte, Türkenschanzstraße 17
  • Thursday 14.03. 09:45 - 11:15 Littrow-Hörsaal Astronomie Sternwarte, Türkenschanzstraße 17
  • Monday 18.03. 11:30 - 13:00 Littrow-Hörsaal Astronomie Sternwarte, Türkenschanzstraße 17
  • Thursday 21.03. 09:45 - 11:15 Littrow-Hörsaal Astronomie Sternwarte, Türkenschanzstraße 17
  • Monday 08.04. 11:30 - 13:00 Littrow-Hörsaal Astronomie Sternwarte, Türkenschanzstraße 17
  • Thursday 11.04. 09:45 - 11:15 Littrow-Hörsaal Astronomie Sternwarte, Türkenschanzstraße 17
  • Monday 15.04. 11:30 - 13:00 Littrow-Hörsaal Astronomie Sternwarte, Türkenschanzstraße 17
  • Thursday 18.04. 09:45 - 11:15 Littrow-Hörsaal Astronomie Sternwarte, Türkenschanzstraße 17
  • Monday 22.04. 11:30 - 13:00 Littrow-Hörsaal Astronomie Sternwarte, Türkenschanzstraße 17
  • Thursday 25.04. 09:45 - 11:15 Littrow-Hörsaal Astronomie Sternwarte, Türkenschanzstraße 17
  • Monday 29.04. 11:30 - 13:00 Littrow-Hörsaal Astronomie Sternwarte, Türkenschanzstraße 17
  • Thursday 02.05. 09:45 - 11:15 Littrow-Hörsaal Astronomie Sternwarte, Türkenschanzstraße 17
  • Monday 06.05. 11:30 - 13:00 Littrow-Hörsaal Astronomie Sternwarte, Türkenschanzstraße 17
  • Monday 13.05. 11:30 - 13:00 Littrow-Hörsaal Astronomie Sternwarte, Türkenschanzstraße 17
  • Thursday 16.05. 09:45 - 11:15 Littrow-Hörsaal Astronomie Sternwarte, Türkenschanzstraße 17
  • Thursday 23.05. 09:45 - 11:15 Littrow-Hörsaal Astronomie Sternwarte, Türkenschanzstraße 17
  • Monday 27.05. 11:30 - 13:00 Littrow-Hörsaal Astronomie Sternwarte, Türkenschanzstraße 17
  • Monday 03.06. 11:30 - 13:00 Littrow-Hörsaal Astronomie Sternwarte, Türkenschanzstraße 17
  • Thursday 06.06. 09:45 - 11:15 Littrow-Hörsaal Astronomie Sternwarte, Türkenschanzstraße 17
  • Monday 10.06. 11:30 - 13:00 Littrow-Hörsaal Astronomie Sternwarte, Türkenschanzstraße 17
  • Thursday 13.06. 09:45 - 11:15 Littrow-Hörsaal Astronomie Sternwarte, Türkenschanzstraße 17
  • Monday 17.06. 11:30 - 13:00 Littrow-Hörsaal Astronomie Sternwarte, Türkenschanzstraße 17
  • Thursday 20.06. 09:45 - 11:15 Littrow-Hörsaal Astronomie Sternwarte, Türkenschanzstraße 17
  • Monday 24.06. 11:30 - 13:00 Littrow-Hörsaal Astronomie Sternwarte, Türkenschanzstraße 17
  • Thursday 27.06. 09:45 - 11:15 Littrow-Hörsaal Astronomie Sternwarte, Türkenschanzstraße 17

Information

Aims, contents and method of the course

A big part of modern astrophysics and astronomy consists in working with large datasets obtained from observatories and supercomputing facilities. This lab course will cover essential aspects of modern statistics, data analysis, and machine learning with a focus to the application to astrophysical problems. The courses will include introductory lectures, followed by hands-on sessions using astrophysical data sets (where possible). The relevant astrophysical concepts will be introduced as well, so that for non-astrophysics majors a key interest will lie in the application of modern methods to relevant astrophysical problems.

Topics will include (sometimes split into multiple units, and not necessarily in chronological order):
- density estimation
- spatial statistics
- data representation and compression, singular value decomposition, autoencoders
- regression
- gaussian processes
- classical machine learning
- supervised machine learning and neural networks
- physics informed neural networks
- generative models
- data visualisation

The final part of the course is dedicated to a ‘focus project’, i.e. an individually carried out small project extending an aspect of the course beyond what is covered.

Assessment and permitted materials

The final mark will be determined based on a series of weekly issued projects/homework problem sets that are to be solved and for which a report has to be written that will be graded; as well as an independent focus project (i.e. a more in-depth follow-up project on one of the topics of the course; possible topics will be mutually agreed upon)

Minimum requirements and assessment criteria

Minimum requirement: at least 50% of points on homework projects, and submission of 4-5 page focus project.

Final mark will come 67% from homework, and 33% from focus project. Homework can be done in groups of 2, focus project must be carried out and submitted individually.

Note that students in the Computational Science Master need to submit only a proportionally reduced number of projects (75%, as they receive only 6 ECTS for this course).

Examination topics

n/a

Reading list

Lecture notes will be provided ahead of each session through moodle.

Association in the course directory

DAT

Last modified: Sa 02.03.2024 10:46