Data Science and Machine Learn
MATH 4322
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
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
Sources:
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