CPSI31003 — Machine Learning I
Add to Bookmarks 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: MATH24004