GDL Course

As part of the African Master’s in Machine Intelligence (AMMI 2021), 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.

All lecture recordings

Lecture 1: Introduction Michael M. Bronstein Recording Slides
Lecture 2: High-Dimensional Learning Joan Bruna Recording Slides
Lecture 3: Geometric Priors I Taco Cohen Recording Slides
Lecture 4: Geometric Priors II Joan Bruna Recording Slides
Lecture 5: Graphs & Sets I Petar Veličković Recording Slides
Lecture 6: Graphs & Sets II Petar Veličković Recording Slides
Lecture 7: Grids Joan Bruna Recording Slides
Lecture 8: Groups Taco Cohen Recording Slides
Lecture 9: Geodesics & Manifolds Michael M. Bronstein Recording Slides
Lecture 10: Gauges Taco Cohen Recording Slides
Lecture 11: Sequences & Time Warping Petar Veličković Recording Slides
Lecture 12: Conclusions Michael M. Bronstein Recording Slides
Tutorial 1: Graph Neural Networks Pim de Haan, Nikola Jovanović Recording Colab
Tutorial 2: Group Equivariant Neural Networks Gabriele Cesa Recording Colab
Seminar 1: Geometric Deep Learning and Reinforcement Learning Elise van der Pol Recording Slides
Seminar 2: Natural Graph Networks Pim de Haan Recording Slides (coming soon!)
Seminar 3: General E(2)-Equivariant Steerable CNNs Gabriele Cesa Recording Slides (coming soon!)
Seminar 4: Weisfeiler and Lehman go Topological: Message Passing Simplicial Networks Fabrizio Frasca Recording (coming soon!) Slides