Supervised Learning

Catalog description

This course is an introduction to machine and statistical learning techniques for making predictions using large and complex data. Supervised learning methods are discussed such as linear and logistic regression, linear discriminant analysis, linear model selection and regularization, decision trees, support vector machines, and artificial neural networks. The uncertainty of the predictions are analyzed using cross-validation and bootstrapping. Prerequisite: grade of "C" or better in CS 120 , MTH 308 , and STAT 305 . Offered Fall.