MATH 4322 - Introduction to Data Science and Machine Learning

Credit Hours: 3
Lecture Contact Hours: 3 Lab Contact Hours: 0

Prerequisite: MATH 3339 or MATH 3349.
Description
Theory and applications for such statistical learning techniques as linear and logistic regression, classification and regression trees, random forests, neutral networks. Other topics might include: fit quality assessment, model validation, resampling methods. R Statistical programming will be used throughout the course.
Repeatability: No

Additional Fee: No