Under construction. This area is a framework being built: most courses have no teaching material yet, and things may change or break. Private self-study. Not an official programme and not recognised by any ministry or school. No qualification, mark or document is issued, and “years 1 to 3” and “advanced” are names of study paths only.
E40 Geometric deep learning and graph neural networks
Advanced pathE: Proposed extensionsplannedabout 100 h (planning estimate)
Planned: syllabus only, no teaching material yet. Nothing on this page can be studied; it describes what the course would cover.
Named in: Advanced applied mathematics pathway
Why this course
Connects groups, symmetry and graphs with networks.
Prerequisites
- Required
- E37 Modern deep-learning architectures, B10 Abstract algebra I: groups, B08 Linear algebra II: abstract structure
- Recommended
- E18 Lie groups and Lie algebras
- Can be taken at the same time
- none
- Everything required, transitively (12 courses)
- B01, B02, B03, B04, B05, B06, B08, B10, B11, E37, F01, F02
- Courses that require this one
- none
Concept-level units
Units follow the syllabus order, as a recommended sequence rather than a gate.
- Symmetry, invariance and equivariance
- Group convolutions
- Graph neural networks and message passing
- Spectral and spatial graph convolutions
- Expressive power and the Weisfeiler-Leman test
- Over-smoothing
- Manifold and mesh networks
- Applications to molecules and physics
Assessable outcome
Prove an equivariance property and place a graph network in the expressiveness hierarchy.
Assessment design
- topic exercises
- mixed problem sets
- unseen proof or derivation problems
- cumulative examination
cumulative; uses spaced retrieval
Resources
Every title here is candidate, unverified unless marked verified. Titles were suggested while planning. Nobody has checked that they exist, what they cost or how they are licensed.
- Bronstein et al · Geometric Deep Learning (free)
- Hamilton · Graph Representation Learning (free)
- Kipf and Welling · Semi-supervised classification with graph convolutional networks (paper)
Content record
- Version
- 0.0.0
- Review
- no review of any kind
- Provenance
- PROPOSED by the assistant on 4 Nov 2026 so that a person with a mathematics degree can read AI articles fluently; not in the owner's brief; APPROVED by the owner on 4 Nov 2026 as an elective; still needs a curriculum reviewer
- Known issues
- Approved as an elective on 4 Nov 2026; syllabus only, no lessons, candidate resources unverified, needs a curriculum reviewer.
- Type
- course
Bridges and laboratories
No bridge or laboratory is attached to this course yet.
You and this course
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