The research group "Reliable Machine Learning" studies the properties of machine learning algorithms. In view of the recent success of deep learningmethods in applications like image recognition, speech recognition, and automatic translation, the group especially focuses on properties of deep neural networks.
Although a neural network trained e.g. for an image classification task might work well on "real inputs", it has been repeatedly shown empirically that such networks are vulnerable to adversarial examples: a minimal perturbation (impercetible to a human) of the input data can cause the network to misclassify the input. Thus, an important research area of the group is to mathematically understand the reasons for the existence of such adversarial examples (i.e., the instability of trained neural networks), and - building on that understanding - to develop improved methods that yield provably robust neural networks.
As part of the Transportation Logistics and Production & Operations Analytics courses, students visited the Audi plant in Ingolstadt last week.
During a guided tour, participants gained valuable insights into the production and logistics processes of one of Europe's largest automotive manufacturing sites. Many of the concepts discussed in class could be observed in practice, ranging from material flows and production processes to the coordination of complex logistics operations.
The visit provided students with an excellent opportunity to connect theoretical concepts with real-world applications and to develop a deeper understanding of the challenges of modern production and logistics systems.
