ECSE 4840 - Introduction to Machine Learning

A broad introduction to statistical machine learning. Topics include supervised learning: generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines; unsupervised learning: clustering, dimensionality reduction, kernel methods; learning theory: bias/variance tradeoffs, practical advice; online learning and reinforcement learning. Recent applications of machine learning, such as to data mining, robot navigation, speech recognition, image processing, and signal processing.

Prerequisites/Corequisites: Prerequisites MATH 2010 and ECSE 2500. Programming skills using MATLAB or Python is preferred.

When Offered: Fall term annually

Credit Hours: 3

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