### COMP 559 - MACHINE LEARNING WITH GRAPHS

**Long Title: **MACHINE LEARNING WITH GRAPHS

**Department: **Computer Science

**Grade Mode: **Standard Letter

**Language of Instruction:** Taught in English

**Course Type: **Lecture

**Credit Hours: **3

**Restrictions: **Must be enrolled in one of the following Level(s):

Graduate

**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.
Mutually Exclusive: Cannot register for COMP 559 if student has credit for COMP 459. Graduate/Undergraduate Equivalency: COMP 459. Recommended Prerequisite(s): Undergraduate-level linear algebra and undergraduate-level probability and statistics Mutually Exclusive: Cannot register for COMP 559 if student has credit for COMP 449/COMP 459.