STAT 452 - Introduction to Statistical Learning

Units: 3
Introduction to statistical machine learning. Supervised learning including linear regression, logistic regression, and classification methods. Unsupervised learning including clustering. Re-sampling methods such as random forests, cross-validation, boosting, and bagging. Applications to data mining, statistical pattern recognition, and data processing.

Prerequisites: One of: STAT 110, STAT 303, STAT 310, STAT 315, STAT 330.
Possible Instructional Methods: On-ground, or Hybrid, or Online-Asynchronous, or Online-Synchronous.
Grading: A-F or CR/NC (student choice).
Course Typically Offered: Spring ONLY

Student Learning Outcomes - Upon successful completion of this course students will be able to:
• Apply fundamental methods for statistical learning, including (a) simple and multiple linear regression, (b) logistic regression, and (c) decision trees and random forests.

• Understand the basic theory and concepts underlying these methods.

• Select statistical models and assess model performance using cross-validation (i.e., splitting data into training and test sets)

• Use R and RStudio to implement statistical learning procedures and analyze complex data sets.

• Communicate statistical learning concepts clearly and appropriately to others.