ISE-5334: Statistical Learning and Data Science

Description: Basic techniques and methods of statistical learning and data science, including statistical learning approaches to solving complex problems with data, problem formulation, analysis methods, and selection of models. Introduction to the general framework, problem formulation, data analytics methods, and modeling strategies of the statistical learning process. Fundamental models and methods for supervised learning, including linear and logistic regression, Bayes classification and discriminant analysis methods, tree-based methods, neural network and deep learning methods. Pre: Graduate Standing.

Pathways: N/A

Course Hours: 3 credits

Corequisites: N/A

Crosslist: N/A

Repeatability: N/A