CPSI 31303 - Machine Learning II

Three credit hours.

This advanced course builds on foundational machine learning concepts to explore optimization, advanced model families, ensemble methods, Bayesian learning, unsupervised learning, and interpretability. Students will study mathematical formulations, parameter estimation, and optimization techniques, and apply advanced models such as support vector machines, random forests, boosting, clustering algorithms, and recommender systems. Ethical considerations including fairness, explainability, and trust in AI systems are explored in depth.

Prerequisites: CPSI 31003