Course Schedule and Catalog

Department: Computer Science

Meeting: 10:50AM - 12:05PM TR (13-JAN-2025 - 25-APR-2025) 

Part of Term: Full Term

Grade Mode: Standard Letter

Course Type: Lecture

Language of Instruction: Taught in English

Method of Instruction: Face to Face

Credit Hours: 3

 

Prerequisites: COMP 382 AND (ELEC 303 OR STAT 310 OR ECON 307 OR STAT 312 OR STAT 315 OR DSCI 301 OR STAT 311) AND (CMOR 302 OR CMOR 303 OR MATH 354 OR MATH 355)

Section Max Enrollment: 40

Section Enrolled: 0

Total Cross-list Max Enrollment: 100

Total Cross-list Enrolled: 0

Enrollment data as of: 14-NOV-2024 11:45AM

 

 

Final Exam: Scheduled Final Exam-OTR Room

 

Description: 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. Mutually Exclusive: Cannot register for COMP 459 if student has credit for COMP 559.