MATH 4322 - Introduction to Data Science and Machine Learning

Credit Hours: 3.0
Lecture Contact Hours: 3 Lab Contact Hours: 0

Prerequisite: MATH 3339.
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.
Additional Fee: N