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|ANLY 512 - Statistical Learning for ANLY|
Basic concepts: Model accuracy, prediction accuracy, interpretability, supervised and unsupervised learning. Linear regression. Classification, logistic regression, linear discriminant analysis. Resampling methods, cross validation. Model selection, dimension reduction, and other high-dimensional considerations. Support vector machines. Unsupervised methods such as PCA and Clustering. If time permits: Splines, general additive models, tree-based methods. Prerequisites: Probabilistic Modeling and Statistical Computing (ANLY-511) or equivalent. Good knowledge of R or Python.
3.000 Credit hours
3.000 Lecture hours
Levels: MN or MC Graduate
Schedule Types: Lecture
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