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Introduction to Statistical Learning

STAT 4360

School of Natural Sciences and Mathematics

Supervised learning methods including linear regression; classification; cross-validation; bootstrap; model selection and regularization; tree-based methods; and support vector machines. Unsupervised learning methods including principal components analysis and clustering. Computer packages such as R or Python will be used for implementation of methods and data analysis. 3 credit hours.

Prerequisite: STAT 3355 or instructor consent required.

Offering Frequency: Each year

Grades: 293

Median GPA: B

Mean GPA: 2.967

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