University of Wisconsin-Madison 3 credits STAT
Multivariate statistical learning methods are essential and broadly applicable tools for analyzing and understanding complex datasets. Emphasizes the conceptual underpinnings, core models, and basic statistical reasoning underlying the techniques. Topics include: dimension reduction (e.g., principal component analysis, multidimensional scaling, factor analysis), clustering (e.g., k-means, hierarchical, density-based, and model-based clustering), and classification (e.g., discriminant analysis, classification tree, and ensemble methods). Utilizes the R programming language.
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