Quarter
Course Type
Course Area
Foundations
Enrollment Code
61820
Location
Phelps 3526
Units
4
Day and Time
T/R 11am-12:50pm
Course Description

 

Machine learning on graphs (static/dynamic, attributed, undirected/directed, single/ensemble) has emerged as an important research topic that finds applications in many domains including social networks, infrastructure design and maintenance, drug discovery, brain networks, and material design. 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 random walks, kernels, spectral analysis, generative models, node embedding, subgraph embedding, and graph neural networks.