Course - Geometric Deep Learning HT26
- Course webpage
- Lecture notes
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Lecture notes (old)
- 1, Introduction
- 2, ML in high dimensions
- 3+4, Geometric priors
- 5, Sets
- 6+7, Graphs
- 8, Graph foundation models (old)
- 9+10, Grids (old)
- 11, Groups and homogenous spaces (old)
- 12, Geometric graphs and geometric GNNs (old)
- 13, Manifolds (old)
- 14, Gauges (old)
- 15, Spectral methods (old)
- 16, Physics-inspired methods (old)
- Lecture recordings
- Other resources:
- Other courses this term: Courses HT26U
- Related courses:
Notes
- Notes - Geometric Deep Learning HT26, Core ideaU
- Notes - Geometric Deep Learning HT26, Mathematical backgroundU
- Notes - Geometric Deep Learning HT26, Invariance and equivarianceU
- Notes - Geometric Deep Learning HT26, Scale separationU
- Notes - Geometric Deep Learning HT26, SetsU
- Notes - Geometric Deep Learning HT26, GraphsU
Practicals
- 1, Invariance
- 2, Graph foundation models (old)
- 3, Spectral transformers (old)
- 4, Invariant and equivariant GNNs (old)
- 5, Neural diffusion (old)