Machine Learning: 3 semester hours

Catalog description

An introduction to machine learning theory and techniques including supervised and unsupervised learning, learning models, theoretical and empirical evaluation. Topics include decision tree, Bayesian learning, instance-based learning, regressions, support vector machine, neural networks, deep learning, reinforcement learning, etc. Prerequisites: ( COMP 2336 or COMP 2013) and ( MATH 3302 or MATH 3023).