Basic Information
The Course Will Cover an Overview of Geometric Representation Learning Spectral Theory, Group Theory, Graph Representation Learning, Graph Neural Networks, Group Neural Networks, Expressive Power of Message Passing Neural Networks, Limitations and Extensions of Graph Neural Networks, Generative Graph Learning Models. at The End of The Semester, Students Will Perform a Small-scale Research Project Related to The Topics Covered During The Course. The Final Delivery Of The Project Will Be a Report Similar to a Research Paper. Learning Outcomes# at The End of The Course The Studetns Will Be Able To# 1. Calculate Basic Spectral Theory Operators Broadly Used in Geometric Learning. 2. Identify Bottlenecks in Geometric Learning Algorithms And Understand How to Avoid Them. 3. Program in Pytorch Geometric Framework. 4. Solve Complex Real-world Tasks Using Deep Geometric Learning Algorithms.__
Faculty: Data and Decision Sciences
|Undergraduate Studies
|Graduate Studies
Pre-required courses
46211 - Deep Learning or 97200 - Deep Learning or 97209 - Machine Learning 2 or 236781 - Deep Learning On Computation
Course with no extra credit
236205 - Advanced Topics Ingeometric Deep Learnin