Foundations of AI in Retail

This unit introduces core concepts and contexts that underpin AI applications in retail, including historical impact, common myths about automation, basic machine learning techniques, compute architectures, and decision frameworks. Students will build conceptual and practical foundations—data quality, model scoring, supervised learning, simulators, OODA loop, and optimization tradeoffs—to prepare for applied analytics and deployment in subsequent units.

Learning Objectives

• Analyze the historical and contemporary impact of AI on retail advertising and operations, including evaluating the Amazon Go narrative to distinguish hype from measurable effects
• Demonstrate foundational machine learning concepts by explaining intelligence, supervised learning, linear regression, model scoring, and the differences between CPU and GPU architectures
• Apply decision-making and simulation frameworks (OODA loop and simulators) to design basic retail analytics workflows that surface core inefficiencies
• Evaluate data quality using the Hierarchy of Data Accuracy and critique a non-convex optimization approach to retail problems, recommending data-driven improvements