Practical Course: Resilient Cognitive Systems:

In this practical course, students have the opportunity to delve into the critical area of assuring the safety of machine-learning-enabled systems. This is a pressing issue in the industry today, particularly in the development of autonomous systems such as robots or self-driving vehicles. That’s why we offer this course in cooperation with the Siemens AG.

During the course, you'll get hands-on experience with a machine-learning-based object detection system designed to replace a light barrier in a safety-critical Cobot-scenario, in which humans and robots should work closely together. This real-world application is far from a theoretical exercise—it poses safety requirements that off-the-shelf ML models can't meet, and you'll learn how to navigate this challenge. You'll follow the complete lifecycle of safety engineering, from hazard analysis and safety analyses to the creation of concrete safety architectures.

But this isn't just about learning—it's about doing. The course is structured as a competition, with Siemens experts offering guidance along the way and scoring your intermediate and final solutions. The team with the most points wins the competition! (Formal grading is separate from the competition and based on evaluation by your examiners) This is a unique chance to learn highly relevant industry knowledge and apply it in a competitive, yet supportive, environment.