ECE-GY 6143 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): Graduate status with undergraduate level probability theory.
Also listed under: CS-GY 6923
Note: May not take if student has already completed EE-UY 4563.

Weekly Lecture Hours: 3

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