Welcome!

This is the GitHub repository for the course:

ECE 594N: Equivariant, Geometric & Topological Deep Learning at UC Santa Barbara.

• Instructor: Prof. Nina Miolane, Geometric Intelligence Lab, UC Santa Barbara.
• Lectures: Mondays, Wednesdays 12:00 - 1:00 PM in PHELP 1431

• Join ECE 594n Slack workspace with your @ucsb.edu email address through the invitation you have received via email.

• Make it a habit to check ECE 594n Slack several times a week.

• Slack is our preferred way of communicating. Avoid emails and use Slack to ask questions about syllabus, lectures, project.

Deep learning has been remarkably successful at solving a massive set of problems on data types including images and text documents. This success drove the extension of this approach to more complex geometric data types, such as graphs, meshes, shape deformations, and more, that arise in real-world data.

The goal of this course is to introduce you to the extensions of deep learning that cover these cases - equivariant, geometric, and topological deep learning - and uncover the geometric principles of intelligence.