Statistical and Machine Learning for Big Data

Catalog description

This course introduces students to many of the important contemporary topics in statistical and machine learning, with an emphasis on big data methods and applications. Both supervised and unsupervised learning techniques will be covered. Topics included in this course are penalized regression and classification procedures, cross-validation, basis expansions, kernel smoothing and regression, model selection, boosting, random forests, support vector machines, clustering, graphical models, and model ensembles. Upon completion of the course, students are expected to have acquired statistical learning techniques that are appropriate for big data applications and have a deep understanding on how to implement these methodologies with software, and more importantly, they will be able to propose data modeling strategies for prediction and/or exploratory analyses, to fit statistical learning models to data by using strategies for tuning and avoiding over fitting, and to combine models into a statistical ensemble. Prerequisite(s): DATS 7100>=C Lecture Hours: 3 Repeatability: May not be repeated for credit. Grade Mode: Normal, Audit Program Restrictions: DPHIL_BIOS, MS_BIOS, MS_DSCI Campus Restrictions: AU Online Schedule Type (Primary): Lecture Schedule Type (Additional): Asynchronous Instruction Click here for the Schedule of Classes.