Machine Learning with Graphs

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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 ) 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. Graduate/Undergraduate Equivalency: COMP 559 . Mutually Exclusive: Cannot register for COMP 459 if student has credit for COMP 559→COMP 459→unlocks 1 courses

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