DATA580

Active

Students have the opportunity to learn how to make sense of data from a variety of sources and learn from it. Aims to answer questions related to the data and in many cases, to make predictions using the data. Discusses both supervised learning as well as unsupervised learning, using the R language in the R-studio environment. Course topics include supervised learning (inference, prediction, bias-variance trade-off, training and test sets, regression, classification, cross-validation, shrinkage methods such as ridge regression and lasso, and subset selection. If time permits, splines, GAMs, trees, support vector machines) and unsupervised learning (principal component analysis and clustering) may be included.

EMFI: Major-Finance Elective, MFIN: Major-Finance, WRMJ: GenEd-Writing in the Major