COMP 459: MACHINE LEARNING WITH GRAPHS
Spring 2024 · Section 001 · CRN 25467
This course will overview both traditional and more recent graph-based machine learning algorithms. Graphs show up in machine learning in many forms. Oftentimes, the input data can be naturally represented as a graph, such as for relational learning tasks applied to social networks and graph kernels applied to chemical data. Other times, graphs are just a framework to express some intrinsic structure in the data, such as for graphical models and non-linear embedding. In both cases, recent advances in representation learning (or graph embedding) and deep learning have generated a renewed interest in machine learning on graphs. At the end of the course, students are expected to be able to: (1) identify the appropriate graph-based machine learning algorithm for a given problem; (2) extend existing algorithms to solve new related problems; and (3) recognize some of the key research challenges in the field. The course will be a mixture of lectures, a research paper presentation, homework assignments (including programming), and a hands-on class project. Cross-list: COMP 559. Recommended Prerequisite(s): Undergraduate-level linear algebra and undergraduate-level probability and statistics Mutually Exclusive: Cannot register for COMP 459 if student has credit for COMP 559.
- Credits
- 3
- Instructors
- Arlei Lopes da Silva