280482 VU WM-c-Dat Data Science in Astrophysics (PI) (2022S)

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).

Registration is open from Tu 08.02.2022 08:00 to We 23.02.2022 12:00
Registration is open from Mo 28.02.2022 08:00 to Tu 15.03.2022 12:00
Deregistration possible until Th 31.03.2022 23:59

Details

max. 30 participants

Language: German

Lecturers

Classes (iCal) - next class is marked with N

Weitere Termine im Computerraum Univ. Sternwarte Do 13:15-16.30
2 Gruppen zu je 15 Personen

Thursday 03.03. 13:15 - 16:30 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
Thursday 10.03. 13:15 - 16:30 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
Thursday 17.03. 13:15 - 16:30 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
Thursday 24.03. 13:15 - 16:30 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
Thursday 31.03. 13:15 - 16:30 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
Thursday 07.04. 13:15 - 16:30 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
Thursday 28.04. 13:15 - 16:30 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
Thursday 05.05. 13:15 - 16:30 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
Thursday 12.05. 13:15 - 16:30 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
Thursday 19.05. 13:15 - 16:30 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
Thursday 02.06. 13:15 - 16:30 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
Thursday 09.06. 13:15 - 16:30 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
Thursday 23.06. 13:15 - 16:30 Seminarraum 1 Astronomie Sternwarte, Türkenschanzstraße 17
Thursday 30.06. 13:15 - 16:30 Seminarraum 1 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. The courses will include introductory lectures, followed by hands-on sessions using astrophysical data sets (where possible).
The tentative lecture plan is as follows:
1. Introduction
2. Basic Statistics and Spatial Statistics
3. Data Representation and Sparsity
4. Density Estimation
5. Regression and Inference 1
6. Regression and Inference 2
7. Gaussian Processes
8. Classical Machine Learning
9. Supervised Machine Learning & Neural Nets 1
10. Supervised Machine Learning & Neural Nets 2
11. Generative Models
12. Data Visualisation
13. Learning Dynamics
14. Focus Project

Assessment and permitted materials

There will be a series of assignments.

Minimum requirements and assessment criteria

Presence (min. 75% for positive mark)
Working program (i.e. runs and produces correct result) (50% required for passing)
Protocols/Homework (50% required for passing)

Examination topics

Reading list

Lecture notes will be distributed during the course through Moodle.

Association in the course directory

Last modified: Th 03.03.2022 16:29