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Geometric Deep Learning:  2022-2023



Schedule C1 (CS&P)Computer Science and Philosophy

Schedule C1Computer Science

Schedule C1Mathematics and Computer Science

Hilary TermMSc in Advanced Computer Science



The course will appeal to students who want to gain a better understanding of modern deep learning and will present a systematic geometric blueprint allowing them to derive popular deep neural network architectures (CNNs, GNNs, Transformers, etc) from the first principles of symmetry and invariance. The focus will be on general principles that underpin deep learning as well as concrete examples of their realisations and applications. The course will try to tie together topics in geometry, group theory and representation learning, graph theory, and machine learning into a coherent picture. It ideally targets students in CS & Math cohort or CS students with a strong mathematical background.

Learning outcomes

● Understand the theoretical geometric principles of symmetry, invariance, and equivariance underlying modern deep learning architectures ● Understand various deep neural network architectures (CNNs, GNNs, Transformers, DeepSets, LSTMs) and be able to derive them from first principles ● Learn different applications of the methods studied in the course and understand problem-specific choices


Students are formally asked for feedback at the end of the course. Students can also submit feedback at any point here. Feedback received here will go to the Head of Academic Administration, and will be dealt with confidentially when being passed on further. All feedback is welcome.