Geometric Deep Learning

Prof. Dr. Marcel Campen

Veranstaltungstyp: Vorlesung und Übung
TeilnehmerInnen:

Beschreibung:
Fundamentals of geometric aspects in machine learning (e.g. invariance, equivariance, multi-scale structure). Overview over representational options (e.g. point clouds, grids, meshes, implicits, parametrics) and corresponding challenges together with advanced technical approaches to learning over and learning of geometric data, in particular 3D objects and scenes. Basic concepts involved in this context include artificial neural networks, convolution, pooling, diffusion, continuous convolution, random walks, transformers, generative approaches. Case studies include shape classification, shape segmentation, shape correspondence, shape generation.

Erstes Treffen:
Dienstag, 02.04.2024 16:00 - 18:00, Ort: 32/109

Ort:
32/109

Semester:
SoSe 2024

Zeiten:
Di. 16:00 - 18:00 (wöchentlich) - Vorlesung, Mi. 14:00 - 16:00 (wöchentlich) - Vorlesung/Übung

Leistungsnachweis:


Veranstaltungsnummer:
6.640

ECTS-Kreditpunkte:
6

Bereichseinordnung:
Veranstaltungen > Informatik > Master of Science in Informatik Veranstaltungen > Informatik > Vorlesungen Courses in English > Mathematics/Computer Science