As part of the African Master’s in Machine Intelligence (AMMI), we have delivered a course on Geometric Deep Learing (GDL100), which closely follows the contents of our GDL proto-book. We make all materials and artefacts from this course publicly available, as companion material for our proto-book, as well as a way to dive deeper into some of the contents for future iterations of the book.
This course was also delivered, with all materials available, in 2021.
All lecture recordings
| Tutorial 1: Introduction to (Expressive) GNNs |
Cristian Bodnar, Iulia Duță, Paul Scherer |
Colab |
| Tutorial 2: Group Equivariant Neural Networks |
Gabriele Cesa |
Colab |
| Tutorial 3: Geometric GNNs |
Charlie Harris, Chaitanya Joshi, Ramon Viñas |
Colab |
| Seminar 1: Graph neural networks through the lens of multi-particle dynamics and gradient flows |
Francesco Di Giovanni |
Recording |
Slides |
| Seminar 2: Subgraphs for more expressive GNNs |
Fabrizio Frasca |
Recording |
Slides |
| Seminar 3: Equivariance in Machine Learning |
Geordie Williamson |
Recording |
Slides |
| Seminar 4: Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs |
Cristian Bodnar |
Recording |
Slides |
| Seminar 5: Highly accurate protein structure prediction with AlphaFold |
Russ Bates |
Recording |
Slides |