About

Statistics 652: Statistical Learning (2 units)

Course Description:

Statistical machine learning overview. Choosing a learning algorithm. Supervised learning including classification methods, nearest neighbors, naive Bayes, decision trees and rules, neural networks, k-means, linear regression and logistic regression. Unsupervised learning including clustering and principle components. 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 or CR/NC (student choice).

Student Learning Outcomes

Upon successful completion of this course students will be able to:

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