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. 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 2022/23 term.
Foundational lecture courses
Special lecture courses
Deep Learning
Advanced Machine Learning: Adversarial Robustness
Foundations of Machine Learning
Causality
Machine Learning
Causal Machine Learning for Business and Organizations
Mathematical Foundations of AI
Design of Experiments
Predictive Modelling
Interpretable Machine Learning
Statistical Foundations of Learning
Machine Learning and IT-Security
Statistical Learning
Mathematics of Reliable AI
Statistical Reasoning & Inference
Reliable and Secure Systems
Techniques in Artificial Intelligence
Uncertainty 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.
Courses fall term 2022/2023
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)
