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 sections list lectures and seminars offered in the fall 2023/24 term and past semesters.

reliable AI Seminar sommer term 2023/24

Mathematics of Deep Neural Networks

reliable AI Seminar fall term 2023/24

Selected Topics in Machine Learning Research

Courses fall term 2023/24

Foundational lecture coursesSpecial 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)
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)Advanced nonlinear control (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)
*Mixture course: counts as Foundational or Special Lecture course.

reliable AI Seminar summer term 2022/23

Mathematics and Reliable AI

Courses summer term 2022/23

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 2022/23

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)