Machine Learning.

Catalog description

Semester course; 3 lecture hours. 3 credits. Prerequisites: CMSC 357 with a minimum grade of C or by permission of the instructor and MATH 310 or MATH 370 with a minimum grade of C. 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 basic concepts and techniques in machine learning. Topics covered will include: probabilistic and optimization-based view of machine learning, linear models, deep nonlinear models, deep networks for specific domains such as large language models and generative models for images.