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 with 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 the list of courses offered with a relevance for this certificate.
The AI-lab is offered in the summer term, since 2023.
Lectures from Physics:
- SS25 Kuhr, Duckeck, Krug: Data Analysis with Machine Learning in Particle Physics
- SS25 Bohrdt: Machine learning for quantum systems
Seminars:
- SS25 Grün, Friedrich, Heng, Gkouvelis: Bayesian Inference and Artificial Intelligence
- SS25 Kepesidis, Gigou, Krausz: The Foundations of Artificial Intelligence: Tracing the Pillars of Modern AI
- SS25 Rulands: Non-equilibrium physics of machine learning
- SS25 Räth: Selected topics from statistical physics and/in AI
Lectures from Computer Science and Statistics:
- SS25 Ommer: Computer Vision: Image & Video Understanding
- SS25 Tresp: Machine Learning
- SS25 Hüllermeier: Preference Learning and Ranking
- SS25 Rügamer: Deep Learning
- SS25 Linnhoff-Popien, Gabor: Natural Computing
- SS25 Schubert: Artificial Intelligence for Games
- SS25 Blanchette: Interactive Theorem Proving
- SS25 Mayer: Artificial Intelligence in Interactive Systems
- SS25 Bischl: Supervised Learning
- SS25 Rezaei, Rügamer: Applied Deep Learning
- SS25 Kranzlmüller: Infrastructure for Advanced Analytics and Machine Learning
- SS25 Ommer: Introduction to Foundation Models (also need to apply on moodle, see links in https://ommer-lab.com/teaching)
Labs:
Lectures from Physics:
- WS24/25 Kepesidis, Krausz Deep Learning for Physicists
- WS24/25 Räth AI in Physics: When Machine Learning Meets Complex Systems
Seminars:
- WS24/25 Rulands Non-equilibrium Physics of artificial intelligence
- WS24/25 Grün, Heng, Friedrich, Gkouvelis Bayesian Inference and Artificial Intelligence
Lectures from Computer Science and Statistics:
- WS24/25 Bothmann Supervised Learning
- WS24/25 Linnhoff-Popien Computational Intelligence
- WS24/25 Tavares Data Mining Algorithmen I
- WS24/25 Schubert Deep Learning and Artificial Intelligence
- WS24/25 Tresp Machine Learning
- WS24/25 Hüllermeier Uncertainty in Artificial Intelligence and Machine Learning
- WS24/25 Aßenmacher Deep Learning for NLP
- WS24/25 Bischl Optimization
- WS24/25 Feurer Automated Machine Learning
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
- SS24 Rulands: Current Advances in 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
Labs:
- SS24 AI Lab experiments:
- Differential equations meet automatic differentiation and efficient computation
- An analytical test of simulation based inference: using generative models for inference in cosmology
- Hyperspectral imaging and compressed sensing
- Phase transitions in stochastic gases driven by deep neural networks
- Gaussian process regression and Bayesian optimization of Laser-Plasma Accelerators
- Forecasting of chaotic systems
- Prediction of students’ physical understanding based on their eye movements during a quantum experiment
- Smart Background Simulation for Particle Physics
- Disease detection using infrared molecular fingerprints
- X-ray cluster mass determination
- Neural networks for many body quantum physics
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. Michael Walther.