Computational Data Science and Machine Learning

4 credits · College of Engineering catalogue

Advisory Prerequisite: None. Enforced Prerequisite: EECS 301 or MATH 425 or STATS 250 or STATS 412 or STATS 426 or IOE 265; No OP/F; or graduate standing. Minimum grade requirement of “C” for enforced prerequisite. Credit Exclusions: Student may only receive credit for one course , ECE 505 and ECE 551. (4 credits) Introduction to computational methods for identifying patterns and outliers in large data sets. Topics include the singular and eigenvalue decomposition, independent component analysis, graph analysis, clustering, linear, regularized, sparse and non-linear model fitting, deep, convolutional and recurrent neural networks. Students program methods; lectures and labs emphasize computational thinking and reasoning.