AI in Physics

Master of Physics with a certificate in Artificial Intelligence

The Faculty of Physics at LMU Munich offers a certificate in Artificial Intelligence (AI) within the the general four-semester program of the Master of Science degree in Physics. This is part of the LMU-wide initiative AIM@LMU. To obtain this certificate with the Master's degree you have to take specialized courses as outlined below in the requirements section.

Requirements for the specialization certificate

The requirements to obtain the Master certificate for the specialisation
in Artificial Intelligence are:

  • 9 ECTS from the AI-Lab
  • 12 ECTS points from two Master level lecture courses as offered by the Computer Science, Mathematics, or Statistics on machine learning (ML).
  • 15 ECTS points from lectures and seminars offered at the physics
    department whith focus on the application of AI or ML methods in the physical sciences.
  • Practical phases and master thesis on a topic at the interface of AI
    and physics. Your supervisor has to decide whether your thesis topic meets these requirements and this has to be indicated when registering for your Master's thesis.

These requirements are in addition to the compulsory components of the Master of Physics course. Information on these regulations can be found here.

The certificate is issued by the Examination Office.

List of Courses

Below is a list of courses offered within physics with a relevance for this certificate. This list is not necessarily complete, please check on an individual level whether the respective course is deemed appropriate for this certificate.

The AI-lab is offered in the summer term, starting from 2023.

Lectures from Physics:

  • SS23 Rulands: Non-equilibrium physics of machine learning (9 ECTS)
  • SS23 Grün: Observing and Data Analysis Mthods for Cosmological Surveys (6 ECTS)
  • WS23/24 Kepesidis: Deep Learning for Physicists
  • WS23/24 Raeth: When machine learning meets complex systems


  • SS23 Kepesidis: Artificial Intelligence in Scientific Research
  • SS23 Kuhr: AI with and for physics
  • WS23/24 Ensslin: Artificial Intelligence, Bayes, and Cognition

Lectures from Computer Science and Statistics:

  • WS23/24 Bengs: Online Machine Learning and Bandits
  • WS23/24 Bischl: Supervised Learning
  • WS23/24 Bischl: Optimization
  • WS23/24 Feurer: Machine Learning Operations
  • WS23/24 Hüllermeier: Uncertainty in Artificial Intelligence and Machine Learning
  • WS23/24 Linnhoff-Popien: Computational Intelligence
  • WS23/24 Schubert: Deep Learning and Artificial Intelligence
  • WS23/24 Seidl: Data Mining Algorithmen I
  • WS23/24 Thomas: Automated Machine Learning
  • WS23/24 Tresp: Machine Learning
  • WS23/24 NN: Deep Learning for NLP


In case of questions please contact Dr Sven Krippendorf.

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