STAT 652 - Statistical Learning

Units: 2
Statistical machine learning overview. Choosing a learning algorithm. Unsupervised learning including classification methods, nearest neighbors, naïve Bayes, decision trees and rules, neural networks, k-means. Supervised learning including linear regression and logistic regression. Model performance and evaluation. Confusion matrix. Report writing.

Prerequisites: Post-baccalaureate standing.
Possible Instructional Methods: Entirely On-ground, or Entirely Online, or Hybrid.
Grading: A-F grading only.
Course Typically Offered: Spring ONLY

Student Learning Outcomes - Upon successful completion of this course students will be able to:

Use software to learn from data.
Critically evaluate learning models.
Extract data from large data source to learn from data.
Create reproducible reports.