ECE276
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ECE 276 - Geometric AI: Equivariant, Topological and Geometric Deep Learning
Electrical and Computer Engineering
College of Engineering
Full Course Title
Geometric AI: Equivariant, Topological and Geometric Deep Learning
Instructor Name(s)
MIOLANE
Course Description
Introduces students to the extensions of deep learning that analyze complex geometric data types, such as graphs, meshes, shapes, and deformations. We present equivariant, geometric, and topological deep learning to uncover the unifying, geometric principles of artificial intelligence.
Unit Value
4
Maximum number of times course can be repeated for additional credit
0
Maximum Units
4
Recommended Preparation
General familiarity with digital/logic design, VLSI circuits/architecture, algorithms, and C/C++/Python programing. Understanding of optimization theory and machine learning theory would be helpful but not required.
Prerequisites
Knowledge of linear algebra, machine learning, deep learning, and python.
Repeat Comments
Previously taught as ECE 594BB during the following quarters: Spring 2022, Winter 2023, Winter 2024. Not open to additional units of credit to student who took ECE 594BB during those quarters.