This course is an introduction to the field of machine learning, covering fundamental techniques for classification, regression, dimensionality reduction, clustering, and model selection. A broad range of algorithms will be covered, such as linear and logistic regression, neural networks, deep learning, support vector machines, tree-based methods, expectation maximization, and principal components analysis. The course will include hands-on exercises with real data from different application areas (e.g. text, audio, images). Students will learn to train and validate machine learning models and analyze their performance. | Knowledge of undergraduate level probability and statistics, linear algebra, and multi-variable calculus. Prerequisite: Graduate standing.

Computer Science (Graduate)
3 credits – 15 Weeks

Sections (Spring 2025)

CS-GY 6923-000 (8191)
01/21/2025 – 05/06/2025 Wed
11:00 AM – 1:00 PM (Morning)
at Brooklyn Campus
Instructed by Sandoval, Gustavo

CS-GY 6923-000 (21706)
01/21/2025 – 05/06/2025 Fri
11:00 AM – 1:00 PM (Morning)
at Brooklyn Campus
Instructed by Rafiey, Akbar

CS-GY 6923-000 (11249)
at ePoly
Instructed by Radhakrishnan, Regunathan

CS-GY 6923-000 (11250)
at ePoly
Instructed by Ghashami, Mina