PREDICTIVE ANALYTICS
Session
Regular Academic Session
Class Number
6805
Career
Graduate
Units
3 units
Grading
GRADUATE GRADING
Description
Principles and methodologies of predictive modeling. Topics include prediction versus interpretation; assessing model accuracy; resampling methods; bootstrapping; subset selection; shrinkage methods; dimension reduction methods; the logistic model; bagging; random forests; principal component analysis; clustering methods. R, SAS, SPSS or a similar software package will be used for data analysis. Prerequisite: MATH 337 or MATH 533.
Class Details
Instructor(s)
Yunwei Cui
Meets
Mo 5:00PM - 7:40PM
Dates
08/26/2019 - 12/17/2019
Room
YR0306
Campus
Main Academic Campus
Location
On Campus
Components
Lecture Required
Class Availability
Status
Open
Seats Taken
19
Seats Open
1
Class Capacity
20
Wait List Total
0
Wait List Capacity
0