CS 4099: ST: Graph Machine Learning
An introduction to models, techniques & algorithms for analyzing and making predictions based on networked (graph) data. Prediction tasks include node classification, link prediction, graph classification, etc. Students will learn about theoretical foundations, building on linear algebra and probability concepts. Additionally, they will gain hands-on experience in working with real-world datasets, where nodes represent non-IID observations. Topics include, but are not limited to: node embeddings, graph neural networks and random graph models. Recommended Background: CS 4342 Machine Learning, MA 2071 Matrices and Linear Algebra I, MA 2621 Probability for Applications.
Jun 6, 2025
