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 – 14 Weeks

Sections (Fall 2024)

CS-GY 6923-000 (16014)
09/03/2024 – 12/12/2024 Thu
11:00 AM – 1:00 PM (Morning)
at Brooklyn Campus
Instructed by

CS-GY 6923-000 (16015)
09/03/2024 – 12/12/2024 Fri
2:00 PM – 4:00 PM (Early afternoon)
at Brooklyn Campus
Instructed by Musco, Christopher

CS-GY 6923-000 (16016)
at ePoly
Instructed by Radhakrishnan, Regunathan

CS-GY 6923-000 (16017)
at ePoly
Instructed by