APPLIED STATISTICS FOR RESEARCH

Catalog description

This course introduces graduate students in data science and engineering to a set of supervised and unsupervised tools for modeling and understanding complex datasets. Topics covered include statistical learning, linear regression, point and interval estimation and hypothesis testing, analysis of variance, classification, linear model selection and regularization, support vector machines, and unsupervised learning. (3 credits) fall