MATH 454 - Data Analysis II - Nonlinear Model Inference

An applied regression course that involves modeling and interpreting data with nonlinear models including K Nearest Neighbors, Logistic Regression, Discriminant Analysis, Bootstrapping, Ridge Regression, LASSO, Principal Components Analysis, Regression Splines, Generalized Additive Models, Tree-Based Models, and Support Vector Machines. While applied, it aims to combine theory and application to emphasize the need for understanding each method’s theoretical foundation. This conversation is had through illustrating a variety of inferences, residual analyses and fully exploring the implications of our assumptions.

Credits: 1.00
When Offered: Spring semester only, in alternate years, based on demand

Corequisite: None
Prerequisites:

MATH 354

Major/Minor Restrictions: None
Class Restriction: None
Area of Inquiry: Natural Sciences & Mathematics
Liberal Arts Practices: The Process of Writing
Core Component: None

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