Tools for the analysis of massive data sets. Topics include: regression, classification trees, clustering, and support vector machines. Extensive use of statistical software. Applications to business, finance, biology, and other sciences and engineering. Notes: Students may not receive credit for both STAT 472 and STAT 572. Cannot be used to satisfy requirements for MS in Statistical Science without prior written approval of the graduate program director.
Applied Statistical Learning
Host University
George Mason University
Semester
Spring 2024
Course Number
STAT 572 DL1
Credits
3
Instructor
Clifton Sutton (csutton@gmu.edu)
Times and Days
Asynchronous
Course Information
Prerequisites
STAT 520 or STAT 521 or STAT 554