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Geometric Deep Learning




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.

Weitere Angaben

Ort: 32/109
Zeiten: Di. 16:00 - 18:00 (wöchentlich) - Vorlesung, Mi. 14:00 - 16:00 (wöchentlich) - Vorlesung/Übung
Erster Termin: Dienstag, 02.04.2024 16:00 - 18:00, Ort: 32/109
Veranstaltungsart: Vorlesung und Seminar (Offizielle Lehrveranstaltungen)


  • Informatik > Master of Science in Informatik
  • Informatik > Vorlesungen
  • Mathematics/Computer Science