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The Osnabrück University offers every Semester a wide variety of courses in the field of Data Science. The specific orientation and focus of the course is in the hands of the responsible lecturer and of course also depends on the degree programme for which it is offered.
For example, the courses offered by Cognitive Science, Computer Science, Econometrics, Psychology, Social Research and working groups in the field of Data Science are generally more application-oriented, while the courses offered at the Institute of Mathematics tend to have a more theoretical focus.
Current term
Geometric Deep Learning
6.640
Dozenten
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.
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)
Studienbereiche
- Informatik > Master of Science in Informatik
- Informatik > Vorlesungen
- Mathematics/Computer Science
