MATH 4323 - Data Science and Statistical 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 maximal marginal classifiers, support vector machines, K-means and hierarchical clustering. Other topics might include: algorithm performance evaluation, cluster validation, data scaling, resampling methods. R Statistical programming will be used throughout the course
Repeatability: No

Additional Fee: No