2018-2019 University Catalogue 
    
    May 18, 2021  
2018-2019 University Catalogue [ARCHIVED CATALOG]

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, Principle 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 methods’ 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
Prerequisites: MATH 354  
Major/Minor Restrictions: None
Class Restriction: None
Area of Inquiry: Natural Sciences & Mathematics
Liberal Arts CORE: None


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