Course - Geometric Deep Learning HT26

Created: October 10, 2025 | Updated: January 21, 2026 | About these notes | View in context | Study these flashcards

---

• Course webpage
• Lecture notes
  • Course schedule
  • 1, Introduction
  • 2, ML in high dimensions
• Lecture notes (old)
  • 1, Introduction
  • 2, ML in high dimensions
  • 3+4, Geometric priors
  • 5, Sets
  • 6+7, Graphs
  • 8, Graph foundation models
  • 9+10, Grids
  • 11, Groups and homogenous spaces
  • 12, Geometric graphs and geometric GNNs
  • 13, Manifolds
  • 14, Gauges
  • 15, Spectral methods
  • 16, Physics-inspired methods
• Lecture recordings
  • 2024-2025
  • 2025-2026
• Other resources:
  • Mathematical Background for GDL
  • Equivariant and coordinate independent convolutional networks
• Other courses this term: [[Courses HT26]]^U

Notes

Practicals

• 1, Invariance (old)
• 2, Graph foundation model (old)
• 3, Spectral transformers (old)
• 4, Invariant and equivariant GNNs (old)
• 5, Neural diffusion (old)

Problem Sheets

• Sheet for lectures 3 & 4, [[Problem Sheet - Geometric Deep Learning HT256, I]]^?
• Sheet for lectures 9 & 10, [[Problem Sheet - Geometric Deep Learning HT256, II]]^?

To-Do List

---

Related posts

• [[Courses HT26]]^U

  (outgoing)
• [[Part C]]^U

  (incoming)
• [[University Notes]]^U

  (incoming)
• [[Course - Computer Vision MT25]]^U

  (sim: 0.656)
• [[Course - Numerical Linear Algebra MT25]]^U

  (sim: 0.657)
• [[Course - Probability MT25]]^U

  (sim: 0.579)