Graph Signal Processing and Geometric Learning is a mathematically intensive course, taught by Prof. Jos ́Moura.
This course presented 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 first section of the course explored how to extend traditional Digital Signal Processing methods to data supported by graphs (Graph Signal Processing) and the second section focused on how to modify the structure of deep learning models to reflect the underlying data geometry (Geometric Learning).
The course had 5 lengthy and mathematically intensive assignments which covered all aspects from GSP to Network science to Graph Deep Learning.
The course also had a final project component.
Our project was Predicting the COVID-19 spread pattern analysis and the factors contributing to it the most and a demographic representation.
PPT link has been attached to the report section.