STT 450 - Statistical Machine Learning

Introduction to fundamental principles and applications of machine learning techniques. Topics include linear regression, classification, re-sampling methods, model selection, tree-based classification methods, and Support Vector Machines (SVM). Students learn how to analyze large/high-dimensional real-world application data to build effective machine learning systems using standard programming tools.

Credit Hours: 3

Prerequisite Courses: STT 215.
Additional Restrictions/Requirements: Prerequisite course and 3 hours of statistics or data science at the 300 level, or consent of instructor.

Course Repeatability: Course may not be repeated.

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