“AI for Retail Executives” Course
Why this course?
To survive as a retailer in 2030 and beyond, all retailers must learn and adopt AI. It is NOT a technology. It is a strategy. And you have to harness it before your competitors do. Additionally, this can not be delegated, it must start with the C-Suite and grow in your organization from there.
AI is not a new technology to pilot, it is a new way of thinking. If you do not understand it, you can not properly leverage it, and if you do not leverage it, your competitors will be able to undercut you on price with higher margins, leading to a dramatic consolidation of the industry.
So Focal has created a number of short courses that are designed for Retail Executives that can teach retailers the core fundamentals of AI and how it can be used in retail. We also offer this course to be taught in person if you contact us! Just let us know!
About the instructor
Francois Chaubard, the course instructor, taught Machine Learning at Stanford for 4 years.
He instructed for one thousand person courses each quarter including the world famous CS 229: Machine Learning with Andrew Ng, where students come in as novices and leave as experts in Machine Learning.
Additionally, he co-created more advanced courses such as CS 224d: Deep Learning for Natural Language Processing and CS231m: Computer Vision for Mobile.
Finally, he was a researcher under Fei-Fei Li in Stanford's AI Lab where he researched state-of-the-art AI.
About the course
The course is broken up into three sections.
First, AI theory where we will teach you the fundamentals of AI.
Second, Application, where we bring you through each function in retail and discuss ways AI can be applied to those functions.
And third, Implementation, where we discuss best practices to make AI work in your organizations.
Lectures
-
Lecture 1: Intro
Explain the course goal and syllabus.
-
Lecture 2: Why "AI for Retail Executives"?
Provide inspiration for why ALL retail executives need this course
-
Lecture 3: Theory - AI Basics
Intro to key terminology, supervised learning, non-supervised, and reinforcement learning.
-
Lecture 4: Theory - Learn Optimization
Learn linear, convex and non-convex optimization (SGD).
-
Lecture 5: Theory - Train your own model!
Exposing Develop an intuition for Deep Learning by training your own model.
-
Lecture 6: Theory - Striving for a "General Theory for Retail".
The 2 SKU grocery store (a toy example) - Exposing the complexity of retail.
-
Lecture 7: Theory - Simulators and AI
Learn about why simulators are so important when building AI systems (in particular when the cost of evaluation/annotation is high).
-
Lecture 8: OODA loops and their application for AI systems
How to implement OODA loops to react quicker to changes in the real world.
-
Lecture 9: Application - AI for checkout
Discuss Amazon Go, Smart Carts, Self-Checkout, and more.
-
Lecture 10: Application - AI for automated inventory
Discuss Shelf-Cams, Robots, Drones, Weight Sensors, Simulation, and more.
-
Lecture 11: Application - AI for demand forecasting
Discuss applications of auto-regressive models for forecasting, supply chain visibility sensors, and advanced ordering systems that adapt to demand.
-
Lecture 12: Application - AI for supply chain
Discuss how to use AI to keep the integrity of your inventory counts. Higher accuracy in IMS means more efficient ordering, saving you millions. AI can help!
-
Lecture 13: Application - AI for pricing
Discuss applications of demand / supply elasticity modeling, RL, and EBITDA maximization.
-
Lecture 14: Application - AI for space planning and planograms
Discuss adaptive POGs, optimal space planning, and retail simulations.
-
Lecture 15: Application - AI for loss prevention
Discuss Computer Vision at self-checkout, shelf-scrape event detection.
-
Lecture 16: Application - AI for E-Commerce
Discuss recommendation engines, robotic picking, online/offline bridging to optimize the customer experience.
-
Lecture 17: Application - AI for Customer Service
Discuss advances in AI for CRM.
-
Lecture 18: Application - AI for Store Management
Discuss shelf digitalization to optimize use of labor and to measure performance.
-
Lecture 19: Application - AI for Warehouse Optimization
Discuss methods of warehouse digitization, optimizing pick labor, warehouse robotics, and more.
-
Lecture 20: Implementation - How to run an AI pilot
Discuss methods for adoption and how to vet solutions, run bakeoffs, structure the POC so you can avoid "wizard of oz" effect.
-
Lecture 21: Implementation - How to bring AI into my organization
Discuss strategies for retail adoption in a large company.
-
Lecture 22: Implementation - Building a Data-Driven Organization
Discuss strategies for retail adoption in a large company.