260072 VU Data Science for Physicists (2022S)
Prüfungsimmanente Lehrveranstaltung
Labels
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Di 01.02.2022 08:00 bis Do 24.02.2022 12:00
- Abmeldung bis Fr 25.03.2022 23:59
Details
max. 75 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine
DO wtl von 10.03.2022 bis 23.06.2022 10.15-11.30 Ort: Ludwig-Boltzmann-Hörsaal, Boltzmanngasse 5, EG, 1090 WienÜbungsanteil in 3 Gruppen:Gruppe 1: DI wtl von 15.03.2022 bis 21.06.2022 08.30-09.30Gruppe 2: DI wtl von 15.03.2022 bis 21.06.2022 09.45-10.45 Gruppe 3: DI wtl von 15.03.2022 bis 21.06.2022 11.00-12.00Ort für diese 3 Gruppen: Kurt-Gödel-Hörsaal, Boltzmanngasse 5, EG, 1090 WienGruppe 4: DI wtl von 15.03.2022 bis 21.06.2022 09.45-10.45 Ort: PC-Seminarraum 1, Kolingasse 14-16, OG01
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 guide students through the basic theoretical concepts regarding Data Science in Physics, and to provide them with the ability to successfully face practical applications in this field. 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.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).
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: Fr 21.10.2022 08:49