Mechanical & Industrial Engrg 522 - PredictvAnalytics&StatLearning

This course will cover statistical methods now widely used in data analysis, learning and prediction such as regression and classification techniques, feature selection, decision trees, and unsupervised learning methods such as clustering and principal components analysis. The emphasis will be on applying the statistical methods to data sets and understanding the optimization theory that drives these methods.

Pre-requisites: Basic knowledge of Calculus (e.g. MATH 233), Linear Algebra (e.g. MATH 235), Probability and Statistics (e.g. MIE 273), and Programming/Problem-solving with Computers (e.g. MIE 124/CompSci 119/ECE122). Python and/or R will be heavily used; either some experience or true willingness to learn it is a must. Resources will be provided to support students. Please contact the instructor if you have any questions or concerns.