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:
- SS24 Grün, Friedrich: From data to insights
- SS24 Kuhr, Duckeck, Hartmann: Data Analysis with Machine Learning in Particle Physics
- SS24 Räth: AI in Physics: When Machine Learning Meets Complex Systems
- SS24 Rulands: Statistical physics of machine learning
Seminars:
- SS24 Grün, Friedrich, Heng, Gkouvelis: Bayesian Inference and Artificial Intelligence
- SS24 Kepesidis, Gigou, Krausz: Causality & Machine Learning
Lectures from Computer Science and Statistics:
- SS24 Ommer: Generative AI and Visual Synthesis
- SS24 Tresp: Machine Learning
- SS24 Hüllermeier: Preference Learning and Ranking
- SS24 Rügamer: Deep Learning
- SS24 Bischl: Supervised Learning
- SS24: Applied Deep Learning
- SS24 Kranzlmüller, Luckow: Advanced Analytics and Machine Learning
- SS24 Linnhoff-Popien, Gabor: Natural Computing
- SS24 Schubert: Artificial Intelligence for Games
- SS24 Blanchette: Interactive Theorem Proving
- SS24 Mayer: Practical Machine Learning
- SS24 Bacho: Deep Learning for Partial Differential Equations
List of Courses (previous semesters):
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
Seminars:
- 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
AI in Physics
AI in Physics
Contact
In case of questions please contact Dr Sven Krippendorf.