Machine Learning for Physicists (PW-ML)5 ECTS
(englische Bezeichnung: Machine Learning for Physicists)
(Prüfungsordnungsmodul: Machine Learning for Physicists)

Lectures:

• • Machine Learning for Physicists
    (Vorlesung, 2 SWS, Florian Marquardt, Mon, 18:00 - 20:00)
  • Machine Learning for Physicists (UE)
    (Übung, 1 SWS, Florian Marquardt et al., Zeit und Raum n.V.)

Inhalt:

This is a course introducing modern techniques of machine learning, especially deep neural networks, to an audience of physicists. Neural networks can be trained to perform diverse challenging tasks, including image recognition and natural language processing, just by training them on many examples. Neural networks have recently achieved spectacular successes, with their performance often surpassing humans. They are now also being considered more and more for applications in physics, ranging from predictions of material properties to analyzing phase transitions. We will cover the basics of neural networks, convolutional networks, autoencoders, restricted Boltzmann machines, and recurrent neural networks, as well as the recently emerging applications in physics. Prerequisites: almost none, except for matrix multiplication and the chain rule.

Lernziele und Kompetenzen:

Learning goals and competences:
Students

• explain the relevant topics of the lecture

• apply the methods to specific examples

Verwendbarkeit des Moduls / Einpassung in den Musterstudienplan:

1. Physik (Bachelor of Science)
   (Po-Vers. 2020w | NatFak | Physik (Bachelor of Science) | Gesamtkonto | Physikalische Wahlfächer | Machine Learning for Physicists)

Studien-/Prüfungsleistungen:

Machine Learning for Physicists (Prüfungsnummer: 668977)

(englischer Titel: Machine learning for physicists)

Prüfungsleistung, Klausur, Dauer (in Minuten): 120, benotet, 5 ECTS

Anteil an der Berechnung der Modulnote: 100.0 %

Prüfungssprache: Englisch

Erstablegung: SS 2021, 1. Wdh.: SS 2021 (nur für Wiederholer)

1. Prüfer:

Florian Marquardt

Termin: 28.07.2021, 10:00 Uhr, Ort: Hörsaal D, Staudtstr. 5, Erlangen