MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS
An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection.
Prerequisites & course information
Requisites: ACT SCI 640 or ( GEN BUS 656 or concurrent enrollment) Course Designation: Core GenEd - Mathematics & Quantitative Reasoning Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Build machine learning models for the purpose of prediction in business contexts, choosing from a variety of approaches. Audience: Both Grad & Undergrad 2. Tune models to optimize predictive performance in business settings. Audience: Both Grad & Undergrad 3. Evaluate and compare prediction accuracy of regression and classification models in business settings. Audience: Both Grad & Undergrad 4. Decompose and demystify machine learning models that underpin AI-driven business solutions. Audience: Both Grad & Undergrad 5. Identify more advanced literature on machine learning for business applications and be able to develop skills in self-study. Audience: Graduate Learning Outcomes: 1. Build machine learning models for the purpose of prediction in business contexts, choosing from a variety of approaches. Audience: Both Grad & Undergrad 2. Tune models to optimize predictive performance in business settings. Audience: Both Grad & Undergrad 3. Evaluate and compare prediction accuracy of regression and classification models in business settings. Audience: Both Grad & Undergrad 4. Decompose and demystify machine learning models that underpin AI-driven business solutions. Audience: Both Grad & Undergrad 5. Identify more advanced literature on machine learning for business applications and be able to develop skills in self-study. Audience: Graduate
Sources
- Course catalogue ↗2026-2027