Description
Introduction to data analytics; data preparation; assessing performance; prediction methods such as decision trees, random forests, support vector machines, neural networks and rules; ensemble methods such as bagging and boosting; clustering techniques such as expectation-maximization, matrix decompositions, and bi-clustering; attribute selection.
Follow-On Courses
This course appears in the pre- or co-requisites for the following course(s):
Learning Hours
120 (36 Lecture, 24 Laboratory, 60 Private Study)
Prerequisite
A cumulative GPA of a 1.70 or higher.
Exclusion
Recommended
Experience with problem solving in any discipline.