STAT 6115 - Statistical Machine Learning with Big Data

A survey of major statistical learning concepts and methods for big data analysis, including both supervised and unsupervised learning such as resampling methods, support vector machines, model selection and regularization, tree-based methods and ensembles, and statistical graphics. Students learn how and when to apply statistical learning techniques, their comparative strengths and weaknesses, and how to critically evaluate the performance of learning algorithms in case studies in financial investment, gene identification, and feature selection in high-dimensional spaces.

Credit Hours: (3)
Restriction(s): Admission to the graduate program and permission of Program Director.
Prerequisite(s): ECON 6113, STAT 5110, STAT 5123, STAT 6113, or permission of department.
Cross-listed Course(s): DSBA 6115.

Schedule of Classes