DSBA 6115 - Statistical 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)
Prerequisite(s): DSBA 5110, STAT 5110, STAT 5123, or permission of department.
Cross-listed Course(s): STAT 6115.

Schedule of Classes