BAYESIAN MACHINE LEARNING FOR MARKETING
Learn to resolve common marketing tasks such as consumer choice prediction, market analysis, and product development by using Bayesian statistical learning methods. A broad range of methods are covered, from standard approaches such as hierarchical modeling to more advanced techniques, including Bayesian Optimization and Bayesian Deep Learning.
Prerequisites & course information
Requisites: Graduate/professional standing and ( GEN BUS 656 , ECON 704 , 709 , STAT 609 , or STAT/MATH 709 ) or declared in graduate Business Exchange program Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Spring 2025 Learning Outcomes: 1. Design and utilize complex models to solve business problems. Audience: Graduate 2. Explain how Bayesian models can be used to model uncertainty in business settings. Audience: Graduate 3. Interpret results and effectively communicate findings in relevant business applications. Audience: Graduate Learning Outcomes: 1. Design and utilize complex models to solve business problems. Audience: Graduate 2. Explain how Bayesian models can be used to model uncertainty in business settings. Audience: Graduate 3. Interpret results and effectively communicate findings in relevant business applications. Audience: Graduate
Sources
- Course catalogue ↗2026-2027