Bayesian Machine Learning for Marketing
This course covers Bayesian statistical learning methods for marketing tasks like consumer choice prediction and market analysis, ranging from hierarchical modeling to Bayesian Deep Learning.
Summary
What you’ll be able to do
Design and utilize complex models to solve business problems.
Explain how Bayesian models can be used to model uncertainty in business settings.
Interpret results and effectively communicate findings in relevant business applications.
Source: UW Guide
Grade history
↗No recorded grade history.
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Prerequisites
Course mapGraduate/professional standing and (GEN BUS 656,ECON 704, 709,STAT 609, or STAT/MATH 709) or declared in graduate Business Exchange program
- Graduate/professional standing
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- declared in graduate Business Exchange program
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- Graduate/professional standing
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- declared in graduate Business Exchange program
Professors
Spring 2027No instructors recorded for this selection.
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Calendar & sections
Spring 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
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Student experience
the class
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.
difficulty & workload
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Topics
- Consumer choice prediction, market analysis, and product development
- Bayesian statistical learning methods
- Hierarchical modeling, Bayesian Optimization, and Bayesian Deep Learning
- Modeling uncertainty in business settings
Skills
- Design and utilize complex models to solve business problems
- Explain how Bayesian models can be used to model uncertainty in business settings
- Interpret results and effectively communicate findings in relevant business applications
Grades
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Catalog & offerings
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Grade history
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Explore recorded gradesRecorded 2026-10-14
Model outputs & technical records
LLM outputs across runs
Full model traces
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Model & dataset provenance
{
"model": "Qwen/Qwen3.8-27B",
"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "01915b83b05b794bcc180504699b5cb055bcedf3659e5ebb718b7c96e56d8ddd",
"requirements_status": "needs_review",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}