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Dec 14, 2024
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2024-2025 Undergraduate Catalog
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DS 303 - Statistical Learning The topics that are discussed in this course are: Basic statistical learning methods such as linear regression and classification; resampling methods such as cross-validation and bootstrapping; model selection methods such as subset selection, ridge and lasso regression, and principle component analysis; tree-based methods such as decision trees and random forests; additional topics may include support vector machines and deep learning. Appropriate R packages are used for the aforementioned methods.
Credits: 4
Prerequisites/Restrictions: DS-203 or ST-201 or Permission of Instructor
Core: Quantitative Reasoning
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