2021-2022 Graduate Catalogue Archived Catalogue
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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.
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