Universität Wien

260072 VU Data Science for Physicists (2024S)

5.00 ECTS (3.00 SWS), SPL 26 - Physik
Prüfungsimmanente Lehrveranstaltung
Moodle

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 75 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

Der VO-Anteil: DO wtl von 07.03.2024 bis 20.06.2024 09.30-11.00 Ort: Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien

Übungsgruppe1: DI wtl von 19.03.2024 bis 18.06.2024 08.45-09.30 Ort: Kurt-Gödel-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien;

Übungsgruppe 2: DI wtl von 19.03.2024 bis 18.06.2024 09.45-10.30 Ort: Kurt-Gödel-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien;

Übungsgruppe 3: DI wtl von 19.03.2024 bis 18.06.2024 10.45-11.30 Ort: Kurt-Gödel-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien

  • Donnerstag 07.03. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
  • Donnerstag 14.03. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
  • Donnerstag 21.03. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
  • Donnerstag 11.04. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
  • Donnerstag 18.04. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
  • Donnerstag 25.04. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
  • Donnerstag 02.05. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
  • Donnerstag 16.05. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
  • Donnerstag 23.05. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
  • Donnerstag 06.06. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
  • Donnerstag 13.06. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
  • Donnerstag 20.06. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

The course focuses on the application of Data Science methods in Physics, that is the combination of interdisciplinary activities (such as scientific, statistical and computational tools) required to elaborate data-centered analysis on relevant physical quantities. Data Science is a topic of increasing interest in the scientific community, due to the growing power of modern computational machines and the associated creation of large databases: The valuable information stored in such large databases can be extracted by Data Science methods, i.e., by combining statistics with advanced computational methods, including machine learning.

This course aims to provide students with an Introduction to the basic theoretical concepts and practical skills oriented towards Data Science in Physics. Specifically, the lectures cover the following topics: (i) collection and manipulation of data via computational tools (mostly in python environments), (ii) effective visualization of relevant information extracted from data, (iii) scientific analysis and physical interpretation of data, (iv) advanced computational techniques (mostly traditional methods, while machine learning techniques will be discussed in the final couple of lectures).

The course is structured in theoretical lectures (on Thursdays), followed by practical lectures (on Tuesdays).

Art der Leistungskontrolle und erlaubte Hilfsmittel

The evaluation of the students takes place continuously, during the practical lectures (on Tuesdays), and by means of Mid-term and End-term tests (on pre-defined Thursdays).

Students are also required to submit a minimum percentage of the weekly proposed exercises.

Mindestanforderungen und Beurteilungsmaßstab

Minimum requirements (before registration):
- Basic but solid knowledge of python coding (e.g., as obtained by the previous programming course in the Bachelor curriculum).

Please note also that UNEXCUSED ABSENCE from the kick-off lecture will lead to immediate deregistration.

Prüfungsstoff

At the end of the course, the students are expected to be familiar with the topic discussed during lectures and to be able to collect data from unstructured sources, to store and efficiently manipulate data, to visually represent the relevant information, to perform rigorous physical interpretation, to reproduce simple machine learning models.

Literatur

S. L. Brunton, and J. N. Kutz, Cambridge University Press (2019), DOI:10.1017/9781108380690
https://doi.org/10.1017/9781108380690

S. Raeisi, and S. Raeisi, Machine Learning For Physicists, IOP Publishing (2023), DOI: 10.1088/978-0-7503-4957-4

P. Mehta, et al., Physics Reports 810, 1-124 (2019), DOI:10.1016/j.physrep.2019.03.001
https://doi.org/10.1016/j.physrep.2019.03.001

M. Nielsen, "Neural Networks and Deep Learning", Determination Press (2015),
http://neuralnetworksanddeeplearning.com/

Zuordnung im Vorlesungsverzeichnis

DSC, UF MA PHYS 01a, UF MA PHYS 01b

Letzte Änderung: Di 05.03.2024 16:06