Statistical Learning

3 credits · Undergraduate

(3 hrs Lecture) This course deals with a variety of topics in statistical learning and their implementation in R. Topics include introduction to statistical learning methods; review of linear regression; use of LASSO and ridge regression techniques to identify useful explanatory variables; understanding the practical difference between predictive outcomes from parametric and non parametric methods; implementation of several ensemble learning methods; clustering methods and dimension reduction; employing reasonable programming practices with basic R syntax and functions; report writing for projects using standard software. Students who major in Data Science are encouraged to take ACS-4953 prior taking this course. Requisite Courses: STAT-3103 [prerequisite(s)].