CPSI 31003 - Machine Learning I

Three credit hours.

This course is an applied introduction to supervised and unsupervised methods. Students learn to frame problems, prepare data, build foundational models (linear/logistic regression, decision trees, k-NN, basic clustering), and evaluate them with appropriate metrics and cross-validation. Emphasis is on controlling overfitting, feature engineering, and representation.

Prerequisites: MATH 24004.