Introduction to Machine Learning.

Catalog description

Semester course; 3 lecture hours. 3 credits. Enrollment is restricted to graduate students in computer science, data science or a related discipline, such as bioinformatics; or by acceptance into the 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 basic concepts and techniques in machine learning, as well as mathematical foundations of these concepts and techniques, and methods to analyze their theoretical properties. 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.