Mar 29, 2024  
2021-2022 Graduate Catalogue 
    
2021-2022 Graduate Catalogue Archived Catalogue

STT 550 - Statistical Data Mining


Course Description: An introduction to the fundamental principles and applications of the most commonly used data mining techniques such as regression, classification, and clustering methods. The data mining techniques may include linear regression, classification, re-sampling methods, linear model selection and regulation, tree-based methods, Support Vector Machines (SVM), and clustering methods. Students will learn how to explore and analyze large high-dimensional real-world applications to build effective systems for prediction by using standard programming tools.

Credit Hours: 3

Corequisite Courses: None
Prerequisite Courses: None
Additional Restrictions/ Requirements: Prerequisites: Undergraduate regression, or experiments design course(s), or consent of instructor.
Course Repeatability: Course may not be repeated Maximum Repeatable Hours: 3


ADDITIONAL COURSE INFORMATION

Equivalent Courses: None
Undergraduate Crosslisting: STT 450
Additional Course Fees: None
Course Attribute: None








Click here for the Spring 2024 Class Schedule.