Units:

12

Description:

This course will present a novel data analytics perspective to deal with data supported by graphs. Such data occurs in many application domains from traditional physics based signals like with time series, image, or video signals to data arising in social networks, marketing, corporate, financial, health care domains. The course shows how to extend traditional Digital Signal Processing methods to data supported by graphs (Graph Signal Processing) and how to modify the structure of deep learning models to reflect the underlying data geometry (Geometric Learning). Topics include: brief review of Linear Algebra concepts; basics on graphs including graph representations through adjacency and graph Laplacian matrices, graph models, and graph spectral analysis; Graph Signal Processing covering topics such as graph shift, graph shift invariance, graph signals, graph filtering, graph Fourier transform, graph convolution and modulation, graph frequency and graph spectral analysis of graph signals, among others; and Geometric Learning to extend deep learning models to learning with data supported by graphs. Students will apply some of the methods presented in class to benchmark datasets and or real world datasets. The course is self-contained; students should have some level of understanding of Linear Algebra.

Last Modified: 2024-06-28 2:39PM

Semesters offered:

• Fall 2024
• Fall 2023
• Fall 2022
• Spring 2020