CS-GY 6923 Machine Learning

3 Credits

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.

Prerequisite(s): Knowledge of undergraduate level probability and statistics, linear algebra, and multi-variable calculus. Graduate Standing.
Also listed under: ECE-GY 6143
Weekly Lecture Hours: 3 | Weekly Lab Hours: 0 | Weekly Recitation Hours: 0