CMPSC 292F Machine Learning on Graphs

Machine learning on graphs has emerged as an important research topic that finds applications in many domains including social networks, drug discovery, brain networks, and other sciences. This course will discuss recent advances in machine learning on graphs including neural network architectures and methods to encode graphs into low-dimensional spaces to facilitate machine learning. Specific topics include neural architectures, representations, explainability, and symmetry.

Once the quarter starts, instructor approval is required to maintain enrollment in the course, including if students do not have the listed pre-requisite courses completed.