Statistical Machine Learning 3,

4 Credits

This course provides a broad introduction to concepts, methods, and practices of statistical machine learning: parametric and nonparametric regression, logistic regression, classification, and basic neural networks; kernel and nearest neighbor estimation, clustering, Bayesian and mixture models. In addition, we will explore selected topics like model selection, cross-validation; PCA, dimension reduction, regularized regression; trees, and ensemble learning. Knowledge of scientific programming in a language such as R required. Prerequisites: ( MATH 205 or MATH 241 or MATH 242 ) and ( MATH 264 or MATH 312 ) and ( MATH 263 or MATH 309 ) Attribute/Distribution: Q