Machine Learning

Catalog description

This course introduces students to fundamental concepts of machine learning and its applications. Hands-on development of unsupervised algorithms (including clustering, dimensionality reduction, and probabilistic methods) and supervised algorithms (including regression, decision trees, neural networks, kernel machines, and ensembles), development and use of machine learning models for various types of application domains will be emphasized. The course develops and applies the linear algebra foundations underlying modern machine learning models, including matrix operations, eigenanalysis, covariance matrices, PCA, and matrix-based formulations of probabilistic models and neural networks. Components: Class Prereqs/Coreqs: P: C- or better in ( COMPUTER 1430 and ELECTENG 3210 ) or ( COMPUTER 1430 and ( MATH 1830 or MATH 4030 ) and ( MATH 2130 or MATH 2730 ) Typically Offered: Fall