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 Thu11:00 AM – 1:00 PM (Morning)at Brooklyn CampusInstructed by
CS-GY 6923-000 (16015)09/03/2024 – 12/12/2024 Fri2:00 PM – 4:00 PM (Early afternoon)at Brooklyn CampusInstructed by Musco, Christopher
CS-GY 6923-000 (16016)at ePolyInstructed by Radhakrishnan, Regunathan
CS-GY 6923-000 (16017)at ePolyInstructed by