Foundations of AI for Retail

This unit introduces foundational AI concepts, computational infrastructure, basic modeling techniques, and the retail business context needed to evaluate and design AI solutions in marketing and operations. Students will review historical shifts in advertising, debunk common automation myths, and learn supervised learning, linear regression, model scoring, simulation, and optimization concepts to identify core retail inefficiencies. The unit prepares learners for hands-on model building, deployment, and strategy development in subsequent units.

Learning Objectives

• Analyze historical and contemporary retail use cases (e.g., advertising changes since 2000, Amazon Go) to assess realistic impacts of AI on store operations and workforce roles
• Demonstrate foundational technical knowledge by comparing CPUs and GPUs, defining types of intelligence, and explaining supervised learning and linear regression using simple examples
• Apply basic modeling and evaluation methods by constructing or outlining a simple linear regression, scoring AI outputs, and describing a supervised learning workflow for a retail marketing problem
• Evaluate retail operational inefficiencies using the hierarchy of data accuracy, simulator concepts, the OODA loop stages, and non‑convex optimization principles to propose data‑driven improvement opportunities