This is the preliminary (or launch) version of the 2021-2022 VCU Bulletin. This edition includes all programs and courses approved by the publication deadline; however we may receive notification of additional program approvals after the launch. The final edition and full PDF version will include these updates and will be available in August prior to the beginning of the fall semester.

CMSC 510. Regularization Methods for Machine Learning. 3 Hours.

Semester course; 3 lecture hours. 3 credits. Enrollment restricted to students with graduate standing in computer science or related discipline such as bioinformatics or acceptance into five-year accelerated program in computer science. The course will assume undergraduate-level background in algorithms, linear algebra, calculus, statistics and probability. Upon successful completion of this course, the student will be able to understand recent advances in machine learning and apply machine-learning tools that go beyond learning from data, as well as have the ability to incorporate additional knowledge about the learning problem. Topics covered will include optimization-based view of supervised machine learning; classical regularization approaches including weight decay and Lasso; regularization terms incorporating additional knowledge about structures in the feature space, including group lasso and graph-based regularization terms; semi-supervised learning using graphs linking unlabeled and labeled samples.