STAT 5270: Statistical Concepts in Machine Learning and Artificial Intelligence
Credits: 3. Contact Hours: Lecture 3.
Prereq: STAT 5101, STAT 5147, and STAT 5279
Introduction to core concepts at the intersection of statistics, machine learning, and artificial intelligence. Topics include bias-variance tradeoff, training and test sets, model assessment and diagnostics, overfitting, error rates, residual analysis, and assumption checking. Statistical frameworks for regularized regression, classification, and ensemble techniques are presented, with attention to feature selection and variable importance. Additional topics may include deep learning, transformers, and generative artificial intelligence. The course emphasizes the statistical interpretation and communication of analytical results to stakeholders through written, oral, visual, and electronic formats.
(Typically Offered: Fall, Summer)