AI in Physics (M.Sc. Physics – specialization certificate)

Lectures from Physics:
Data Analysis with Machine Learning in Particle Physics(Kuhr, Duckeck)
Introduction to Machine Learning for Physics (Grün, Homer)
Statistical physics of Artificial Intelligence (Rulands)

Seminars:
Bayesian Inference and Artificial Intelligence (Grün, Heng, Friedrich)
The Foundations of Artificial Intelligence: Tracing the Pillars of Modern AI (Kepesides, Krausz)
Machine Learning for Quantum Systems (Bohrdt)

Lectures from Computer Science and Statistics:
Deep Learning for NLP (Aßenmacher)
Generative AI and Visual Synthesis (Ommer, Phan)
Machine Learning II: Modern AI Systems (Tresp)
Preference Learning and Ranking (Hüllermeier, Sale)
Deep Learning (Rügamer, Rezaei)
Artificial Intelligence for Games (Schubert)
Interactive Theorem Proving (Xu, Blanchette, Graß)
Applied Deep Learning (Rezaei, Rügamer)
Infrastructure for Advanced Analytics and Machine Learning (Kranzlmüller)

Labs:
AI lab (also need to apply on Moodle)
Experiments:
- X-ray cluster mass determination
- Neural networks for many body quantum physics
- Prediction of students’ physical understanding based on their eye movements during a quantum experiment
- Simulation-based inference with random fields in cosmology
- Smart Background Simulation for Particle Physics
- Forecasting of chaotic systems
- Emergent weight morphologies in deep neural networks
- Using quantum computers to solve machine learning tasks

Lectures from Physics:

• Deep Learning for Physicists (Kepesidis, Krausz)
• AI in Physics: When Machine Learning Meets Complex Systems (Räth)
• Inverse problems and machine learning in medical physics (Gianoli)

Seminars:

• Bayesian Inference and Artificial Intelligence (Grün, Heng, Friedrich, Gkouvelis)

Lectures from Computer Science and Statistics:
(note: the main registration for CS courses happens from 1.9.-30.9.2025 on LSF now, late registration for courses without limitations is from 10.10.-30.10.2025)

• Deep Learning for NLP (Aßenmacher)
• Data Mining Algorithmen I (Seidl, Marques Tavares)
• Optimization (Bischl, Schneider)
• Applied Deep Learning (Rügamer, Rezaei)
• Deep Learning and Artificial Intelligence (Schubert)
• Computational Intelligence (Gabor)
• Uncertainty in Artificial Intelligence and Machine Learning (Hüllermeier)
• Intelligent Systems (Bernabé-Moreno)

Lectures from Physics:

• Data Analysis with Machine Learning in Particle Physics (Kuhr, Duckeck, Krug)
• Machine learning for quantum systems (Bohrdt)

Seminars:

• Bayesian Inference and Artificial Intelligence (Grün, Friedrich, Heng, Gkouvelis)
• The Foundations of Artificial Intelligence: Tracing the Pillars of Modern AI (Kepesidis, Gigou, Krausz)
• Non-equilibrium physics of machine learning (Rulands)
• Selected topics from statistical physics and/in AI (Räth)

Lectures from Computer Science and Statistics:

• Computer Vision: Image & Video Understanding (Ommer)
• Machine Learning (Tresp)
• Preference Learning and Ranking (Hüllermeier)
• Deep Learning (Rügamer)
• Natural Computing (Linnhoff-Popien, Gabor)
• Artificial Intelligence for Games (Schubert)
• Interactive Theorem Proving (Blanchette)
• Artificial Intelligence in Interactive Systems (Mayer)
• Supervised Learning (Bischl)
• Applied Deep Learning (Rezaei, Rügamer)
• Infrastructure for Advanced Analytics and Machine Learning (Kranzlmüller)
• Introduction to Foundation Models (also need to apply on moodle, see links in https://ommer-lab.com/teaching) (Ommer)

Labs:

• AI Lab (also need to apply on moodle)
  • Experiments:
    • X-ray cluster mass determination
    • Neural networks for many body quantum physics
    • Prediction of students’ physical understanding based on their eye movements during a quantum experiment
    • Disease detection using infrared molecular fingerprints
    • Simulation-based inference with random fields in cosmology
    • Smart Background Simulation for Particle Physics
    • Forecasting of chaotic systems
    • Differential equations meet automatic differentiation and efficient computation
    • Emergent weight morphologies in deep neural networks
    • Using quantum computers to solve machine learning tasks
    • Bayesian Inference for Spectroscopic Reconstruction

Lectures from Physics:

• Deep Learning for Physicists (Kepesidis, Krausz)
• AI in Physics: When Machine Learning Meets Complex Systems (Räth)

Seminars:

• Non-equilibrium Physics of artificial intelligence (Rulands)
• Bayesian Inference and Artificial Intelligence (Grün, Heng, Friedrich, Gkouvelis)

Lectures from Computer Science and Statistics:

• Supervised Learning (Bothmann)
• Computational Intelligence (Linnhoff-Popien)
• Data Mining Algorithmen I (Tavares)
• Deep Learning and Artificial Intelligence (Schubert)
• Machine Learning (Tresp)
• Uncertainty in Artificial Intelligence and Machine Learning (Hüllermeier)
• Deep Learning for NLP (Aßenmacher)
• Optimization (Bischl)
• Automated Machine Learning (Feurer)

Lectures from Physics:

• From data to insights (Grün, Friedrich)
• Data Analysis with Machine Learning in Particle Physics (Kuhr, Duckeck, Hartmann)
• AI in Physics: When Machine Learning Meets Complex Systems (Räth)
• Statistical physics of machine learning (Rulands)

Seminars:

• Bayesian Inference and Artificial Intelligence (Grün, Friedrich, Heng, Gkouvelis)
• Causality & Machine Learning (Kepesidis, Gigou, Krausz)
• Current Advances in Machine Learning (Rulands)

Lectures from Computer Science and Statistics:

• Generative AI and Visual Synthesis (Ommer)
• Machine Learning (Tresp)
• Preference Learning and Ranking (Hüllermeier)
• Deep Learning (Rügamer)
• Supervised Learning (Bischl)
• Applied Deep Learning
• Advanced Analytics and Machine Learning (Kranzlmüller, Luckow)
• Natural Computing (Linnhoff-Popien, Gabor)
• Artificial Intelligence for Games (Schubert)
• Interactive Theorem Proving (Blanchette)
• Practical Machine Learning (Mayer)
• Deep Learning for Partial Differential Equations (Bacho)

Labs:

• 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