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.
Insights into Warehouse Logistics at EDEKA Südbayern
As part of the Retail Management course, students visited EDEKA Südbayern.
During the visit, Dr. Markus Frank, a former WFI PhD student who now works at EDEKA, provided valuable insights into warehouse logistics at EDEKA Südbayern. Students gained a practical understanding of the challenges and processes of modern retail logistics, ranging from product supply and inventory management to the efficient coordination of warehousing and distribution operations.
The visit offered an excellent opportunity to connect theoretical course content with practical applications in food retailing and to gain first-hand insights into the daily operations of a leading retail company.
