Deep Learning for Vision – Foundations and Applications (WS 26/27)

This course provides a comprehensive introduction to the key concepts and architectures underlying modern deep learning for image data. It covers the mathematical and computational foundations of  vision architectures such as convolutional networks, vision transformers, and hybrid architectures. Students study the principles behind image classification, segmentation, detection, and tracking, along with low-level tasks like denoising and super-resolution. The course further will give an introduction into advanced topics such as self-supervised representation learning, generative modeling, and multimodal vision, emphasizing conceptual understanding and the unifying ideas that drive state-of-the-art vision research.

Modules:

• CMS-AAI-AP
• CMS-VC-ELV1
• CMS-TRK-DLVFA
• INF-25-MA-FVC

Location: Schumannbau SCH/A117. 
Lecture Time:  Wed 3 DS (11:10-12:40)
Exercises: Tue 4DS (1pm-2:30pm)
(other options available too depending on students availability, will be discussed during first lecture)


First Lecture (+ coordination)
Oct 21st 2026, 11:10-12:40 at SCH/A117


Course github: https://github.com/weigertlab/DLVFA