Welcome to the Embodied AI Research Seminar (class 2022/23)
The course will be scheduled to the individual needs of each course. Hands on! We speak Python!
“There are two possible outcomes: if your result confirms your set hypothesis, then you have made a measurement. If the result is contrary to your hypothesis … then you have made a novel discovery” (attributed to Enrico Fermi (1901-1954))
Slides Part 1 (3,506 kB) current as of 30.05.2022 08:00 CET HCAI-220530-Introduction-Motivation
Slides Part 2 (12,436 kB) current as of 30.05.2022 08:00 CET 2-HCAI-220530-Reinforcement-Learning
Module 01: Introduction to Human-Centered AI
In the first part the students get a rough overview on the concepts of human-centered AI and some currently hot topics from Artificial Intelligence and Machine Learning to get a good common understanding and basis for further research in our areas. Central questions include: What is Human-Centered AI and why is it important? What is integrative machine learning? Why is the health domain complex? What is probabilistic information? What is the difference between autonomous ML and interactive machine learning? What is the human-in-the-loop supposed to do? What is the difference between Causality and Causability? What is ground truth and why do we need grond truth?
Topic 01: The HCAI approach: Towards integrative AI/ML
Topic 02: The complexity of the application area Health Informatics
Topic 03: Probabilistic Information and Probabilistic Learning
Topic 04: Gaussian Processes
Topic 05: Automatic Machine Learning (aML)
Topic 06: Interactive Machine Learning (iML)
Topic 07: Causality, Explainability, Interpretability, Causability
Topic 08: The #KandinskyPatterns Exploration Environment
Lecture slides 2×2 (pdf, 9,996 kB):
Lecture slides full size (pdf, 6,121 kB):
Note: The slides provided here are for printing and reading, the slides shown will be different from a didactial point of view.