STAT 574 - Supervised Machine Learning

(3 units)

Prerequisite: STAT 510, or consent of instructor.
Decision trees for regression, binary, and multinomial classification; random forests, gradient boosting, K nearest-neighbor algorithm, support vector machines, naïve Bayes classification, artificial neural network, recurrent neural network, anomaly detection, change-point detection, convolutional neural network, natural language processing. The choice of software (SAS, Enterprise Miner, R, and/or Python) is at the instructor’s discretion.

Letter grade only (A-F). (Lecture 3 hours)