DSCI 425 - Supervised Learning

(3 credits)

An introduction to machine learning and statistical learning techniques for predicting both numeric and categorical outcomes. Topics include linear and non-parametric methods for regression and classification, resampling techniques for model validation, regularization to control model complexity, and advanced methods such as tree-based algorithms, support vector machines, and deep learning. Prerequisites: MATH 222 - Data-Driven Mathematical Modeling or MATH 311 - Linear Algebra; △ STAT 360 - Regression Analysis and ◆ DSCI 325 - Management of Structured Data; or instructor permission. Grade or P/NC. Offered alternate years.

Course Registration