Lectures & Seminars

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 lecture courses offer knowledge about the algorithmic and mathematical aspects of AI, while special lecture courses focus on reliable AI and cover the topics of the three core application domains. The following section lists typical courses for each section. Further below you can find a list of courses offered in the fall and sommer 2022/23 terms.

Foundational lecture coursesSpecial lecture courses
Deep LearningAdvanced Machine Learning: Adversarial Robustness
Foundations of Machine LearningCausality
Machine LearningCausal Machine Learning for Business and Organizations
Mathematical Foundations of AIDesign of Experiments
Predictive ModellingInterpretable Machine Learning
Statistical Foundations of LearningMachine Learning and IT-Security
Statistical LearningMathematics of Reliable AI
Statistical Reasoning & InferenceReliable and Secure Systems
Techniques in Artificial IntelligenceUncertainty in AI and Machine Learning along with Adaptive Control
AI for Medicine
Fairness in Automated Decision Making
Formal Methods for Cyber-Physical Systems
Social Choice Theory
Trustworthy and Privacy-Preserving AI in Healthcare
This list consists of courses that relAI fellows offer regularly. New courses may join the list at any time.

reliable AI Seminar summer term 2023

Mathematics and Reliable AI

Courses summer term 2023

Foundational lecture coursesSpecial lecture courses
Graphical Models in Statistics (TUM)Cyber-Physical Systemsn (TUM)
Machine Learning for Graphs and Sequential Data (TUM)*Machine Learning for Graphs and Sequential Data (TUM)*
Advanced Machine Learning: Deep Generative Models (TUM)*Advanced Machine Learning: Deep Generative Models (TUM)*
Mathematics of Artificial Intelligence (LMU)Introduction to Human and Robotic Hand grasping: Control and Manipulation (TUM)
Dynamical Systems (TUM)*Dynamical Systems (TUM)*
Fairness in Machine Learning (LMU)*Fairness in Machine Learning (LMU)*
Supervised Learning (aka Predictive Modelling) (LMU)AI in Medicine I (TUM)
Introduction to Machine Learning (LMU)AI in Medicine II (TUM)
Advanced Machine Learning (LMU)ChatGPT and Its Ilk (LMU)
Deep Learning (LMU)
Machine Learning and Deep Learning with Python (LMU)
Statistical Learning Theory (LMU)
Preference Learning and Ranking (LMU)
Automated Algorithm Configuration and Design (LMU)
Optimization for Machine Learning (LMU)
*Mixture course: counts as Foundational or Special Lecture course.

Courses fall term 2022/2023

Foundational lecture coursesSpecial 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)