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260072 VU Data Science for Physicists (2023S)
Prüfungsimmanente Lehrveranstaltung
Labels
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mi 01.02.2023 08:00 bis Do 23.02.2023 07:00
- Abmeldung bis Fr 31.03.2023 23:59
Details
max. 75 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
Kickoff: 09.03.2023 09:30 - 11:00 (Ludwig-Boltzmann-Hörsaal)
Vorlesungsanteil: Do 09:30-11:00 Uhr (Ludwig-Boltzmann-Hörsaal)4 Übungsgruppen:Gruppe 1: DI wtl von 12.03.2023 bis 20.06.2023 08.45-09.30 Ort: Kurt-Gödel-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien;Gruppe 2: DI wtl von 12.03.2023 bis 20.06.2023 09.45-10.30 Ort: Kurt-Gödel-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien;Gruppe 3: DI wtl von 12.03.2023 bis 20.06.2023 10.45-11.30 Ort: Kurt-Gödel-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien;Gruppe 4: DI wtl von 12.03.2023 bis 20.06.2023 11.45-12.30 Ort: Kurt-Gödel-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
- Donnerstag 09.03. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
- Donnerstag 16.03. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
- Donnerstag 23.03. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
- Donnerstag 30.03. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
- Donnerstag 20.04. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
- Donnerstag 27.04. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
- Donnerstag 04.05. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
- Donnerstag 11.05. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
- Donnerstag 25.05. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
- Donnerstag 01.06. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
- Donnerstag 15.06. 09:30 - 11:00 Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 Wien
- Donnerstag 22.06. 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.
- 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/10.1017/9781108380690P. Mehta, et al., Physics Reports 810, 1-124 (2019), DOI:10.1016/j.physrep.2019.03.001
https://doi.org/10.1016/j.physrep.2019.03.001M. Nielsen, "Neural Networks and Deep Learning", Determination Press (2015),
http://neuralnetworksanddeeplearning.com/
https://doi.org/10.1017/9781108380690P. Mehta, et al., Physics Reports 810, 1-124 (2019), DOI:10.1016/j.physrep.2019.03.001
https://doi.org/10.1016/j.physrep.2019.03.001M. 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 30.01.2024 17:46