EAS 508LEC - Statistical Learning and Data Mining I

An introduction to the mathematical theory and computational methodology at the heart of statistical learning. This first semester considers supervised learning, including topics classification – support vector machines, k-nearest neighbors, Naive Bayes, logistic regression, tree methods and forests, bagging and ensemble methods, GPs and neural networks – and methods for validation and testing. R programming will be used. Prerequisite experience: EAS MS-DS student, or MTH 142, MTH 309, MTH 411 or equivalent, or permission of instructor.

Credits: 3