Statistical Learning for ANLY

3 Credits · Level: Graduate, Undergraduate

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.