Lectures and seminares addressing themes of reliable AI constitute a central part of the relAI curriculum for MSc students or offer an opportunity for newly joining doctoral researchers to fill potential knowledge gaps or get updated on the latest findings. The reliable AI seminar is specifically tailored to relAI students. Foundational courses offer knowledge about the algorithmic and mathematical aspects of AI, while special courses focus on reliable AI and cover the topics of the three core application domains. The following sections list lectures, seminars, and practical courses offered in the fall 2024/25 term and past semesters.
reliable AI Seminar fall term 2024/25
Courses fall term 2024/25
| Foundational lectures | Special lectures |
|---|---|
| Fundamentals of Artificial Intelligence (TUM) | Formal Methods for Cyber-Physical Systems (TUM) |
| Machine Learning (TUM) | Artificial Intelligence in Medicine (TUM) |
| Causality (TUM) | Automated Machine Learning (LMU) | Supervised Learning (LMU) | Introduction to Intelligent User Interfaces (LMU) |
| Optimization (LMU) | Computational Social Science (LMU) |
| Lifetime Data Analysis (LMU) | Remote Machine Intelligence Lab (TUM) |
| Deep Learning for NLP (DL4NLP) (LMU) |
| Foundational seminars and practical courses | Special seminars and practical courses |
|---|---|
| Fundamentals of Mathematical Statistics (TUM) | Master Practical: Reinforcement Learning and Benchmarking for Modular Robots (TUM) |
| Theoretical advances in deep learning (TUM) | Confidential Cloud Computing (TUM) |
| Causality and Machine Learning (TUM) | Trustworthy AI for Medicine (TUM) | Deep Learning (TUM) | Human-Centered Technologies for Learning (TUM) |
| Master Practical: Visual Representation Learning (LMU)* | Master Practical: Visual Representation Learning (LMU)* |
| Master Practical: Image & Video Synthesis (LMU)* | Image & Video Synthesis (LMU)* |
| Responsible Data Science for Safe and Socially Aligned AI (TUM) | |
| Multi-modal AI for Medicine (TUM) | |
| AI for Vision-Language Models in Medical Imaging (TUM) | |
| From Self-supervised Learning to Foundation Models in Medical Imaging (TUM) | |
| Optimal control and Reinforcement Learning for Robotics (TUM) | |
| Machine Learning in Neuroimaging (TUM) | Quantum Software Systems (TUM) |
reliable AI Seminar sommer term 2023/24
Courses sommer term 2023/24
| Foundational lectures | Special lectures |
|---|---|
| Supervised Learning (LMU) | Advanced Methods in Social Statistics and Social Data Science (LMU) |
| Introduction to Machine Learning (LMU) | Social Choice Theory (LMU) |
| Deep Learning (LMU) | Artificial Intelligence in Medicine II (TUM) | Applied Deep Learning (LMU) | Medical Imaging Technology (TUM) |
| Preference Learning and Ranking (LMU) | Advanced Machine Learning: Deep Generative Models (TUM) |
| Machine Learning (LMU) | Statistical Foundations of Learning (TUM)Computer Science for Medical Students (TUM) |
| Statistical Foundations of Learning (TUM) | Computer Aided Medical Procedures II (TUM) |
| Applied Machine Learning (LMU)* | Applied Machine Learning (LMU)* |
| Machine Learning for Graphs and Sequential Data (TUM)* | Machine Learning for Graphs and Sequential Data (TUM)* |
| Dynamic Systems (TUM) | |
reliable AI Seminar fall term 2023/24
Courses fall term 2023/24
| Foundational lecture courses | Special lecture courses |
|---|---|
| Machine Learning (LMU) | Artificial Intelligence in Medicine (TUM) |
| Master Seminar "Foundation Models in AI" (LMU) | Uncertainty in Artificial Intelligence and Machine Learning (LMU) |
| Advanced Student Seminar: Deep Learning (TUM) | Secure and Reliable Systems (TUM) | High-Dimensional Probability (LMU) | Advanced nonlinear control (LMU) |
| Applied Harmonic Analysis (LMU)* | Applied Harmonic Analysis (LMU)* |
| Mathematics of Artificial Intelligence (LMU) | AI for Good (LMU) |
| Causality (TUM)* | Causality (TUM)* |
| Master-Seminar - Causality and Machine Learning (TUM) | Master Seminar "Machine Learning with Knowledge Graphs" (LMU) |
| Inferenzstatistik I: Grundlagen der Schätztheorie (LMU) | |
| Online Machine Learning and Bandits (LMU) | |
| Automated Algorithm Configuration and Design (LMU) | |
| Control Systems 2 (TUM) | |
| Machine Learning and Optimization (TUM) | |
| Seminar Machine Learning (TUM) | |
| Machine Learning (TUM) | |
| "Master-Seminar“ Theoretical advances in deep learning (TUM) |
reliable AI Seminar summer term 2022/23
Courses summer term 2022/23
Courses fall term 2022/23
| Foundational lecture courses | Special lecture courses |
|---|---|
| Techniques in Artificial Intelligence (TUM) | Automated Machine Learning (LMU) |
| Supervised Learning (LMU) | Formal Methods for Cyber-Physical Systems (TUM) |
| Lifetime Data Analysis (LMU) | Introduction to Intelligent User Interfaces (LMU) |
| Deep Learning 4 NLP (LMU) | Responsible Robotics 1: Prerequisites and requirements for a society of long life (TUM) |
| Machine Learning (TUM) | Advanced Robotic Perception (TUM) |
| Advanced Deep Learning (LMU) | Optimal Control and Decision Making (TUM) |
| Statistical Reasoning and Inference - for Science and Data Science (LMU) | Uncertainty in Artificial intelligence and Machine Learning (LMU) |
| Online Machine Learning and Bandit Algorithms (LMU) | AI in Medicine I (TUM) |