Students will learn foundational concepts and techniques in statistical learning focusing on building and evaluating models for regression and classification problems. Topics will include data preprocessing, error/accuracy metrics, data splitting, cross-validation, and model selection. These topics will be presented through the use of linear/logistic regression, penalized regression, k-nearest neighbors, and tree-based models. Additional topics may include, but are not limited to, clustering, neural networks, PCA, PCR, support vector machines, and splines/smoothing. Students will work on projects requiring them to select and use appropriate tools for solving open-ended problems. Communication of results to various audiences will be emphasized. Prerequisites: DATA 201, and MATH 203 is recommended.
Grade Basis: Letter Grade
Credits: 4.0
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