Machine Learning

Catalog description

Credits 3. 3 Lecture Hours. Theoretical foundations of machine learning, pattern recognition and generating predictive models and classifiers from data; includes methods for supervised and unsupervised learning (decision trees, linear discriminants, neural networks, Gaussian models, non-parametric models, clustering, dimensionality reduction, deep learning), optimization procedures and statistical inference. Prerequisite: Grade of C or better in MATH 304 , MATH 311 , or MATH 323 ; Grade of C or better in STAT 211 , and STAT 404 or CSCE 221 , or ECEN 303 , and CSCE 121 or CSCE 120 . Cross Listing: CSCE 421 and ECEN 427 .