The Foundations of Graph Neural Networks course covers a broad set of material that balances theoretical depth with practical hands-on engineering. It covers topics like:

Theory

• Neural Message Passing
• Fourier Transforms, Graph Wavelets and Spectral Convolutions
• Permutation Symmetries
• Representational capacity of GNNs
• Graph fundamentals like the Laplacian and graph isomorphism

Hands-On

• Complete examples with working code of Node Classification, Graph Classification, and Link Prediction problems
• An overview of tools of the trade
• A discussion of common engineering challenges, like scalability and over-smoothing, along with approaches for overcoming them

This course will consist of video lectures from me, fully coded examples with videos that walk through them in detail, quizzes to test your understanding of key concepts, and a dedicated Discord channel for peer discussion.

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Learn more in the Wecome Video:

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