Reinforcement learning is a branch of machine learning or artificial intelligence that is loosely based on how humans and animals learn through interaction with their environment. As children we used random trial and error to take actions and observe the cause and effect. We stored our experiences and through repetition reinforced the cause and effect learning resulting in memories allowing us to recall what actions to take in similar situations. All through our lives we continue to learn in this manner. This tutorial will define the differences between supervised learning and reinforcement learning. Several toy examples will be presented to illustrate the mathematics behind reinforcement learning. Different types of reinforcement learning will be described including model-based learning and several types of model free learning. The tutorial will also present how Daisy uses simulation-based reinforcement learning based on Daisy's proprietary theory of retail, a set of couple partial differential equations that explain retail dynamics. Daisy's uses this approach to assist retailers to make smarter merchandising decisions including what products to promote each week, what prices to charge for each product at each store and how much inventory of each product to allocate to each store and distribution centre. Daisy's methods are presented through real-world client examples and the disruptive financial results are shared. The tutorial ends with a future vision of the autonomous enterprise with research areas that need to be resolved to fulfill the vision.

Gary is one of North America's preeminent experts in artificial intelligence having over 25 years' experience working with leading global corporations to deliver revenue and profit growth. He founded Daisy Intelligence in 2003 bringing autonomous machine intelligence to clients in retail, insurance and healthcare. Daisy Intelligence (www.daisyintelligence.com), headquartered in the Greater Toronto Area operates an applied artificial intelligence (A.I.) software-as-a-service (SaaS) business delivering operational corporate decisions that are too complex for humans to make, resulting in efficiencies and profitability gains.

Currently, Daisy is revolutionizing optimization of merchandise planning for high volume retailers and fraud detection/risk management for insurance and banking. Using proprietary mathematical solutions and Daisy's reinforcement learning based A.I. simulation platform, Daisy analyzes trillions of the trade-offs inherent in any complex business question and provides timely, actionable decision recommendations to help corporate clients grow total sales, improve margins and reduce fraud.

Daisy was recognized in 2018 as a Gartner Cool Vendor in A.I. for retail, won startup of the year (2018) at the AIconics conference in Silicon Valley from a field of over 100 global entrants and won a $5 million term sheet at the Elevate A.I. conference in Toronto.

Gary is the former head of IBM Canada's data mining and data warehousing practices. He was also at the helm of Loyalty Consulting Group, providing analytical services for one of the world's most successful coalition loyalty programs, the AIR MILES® Reward Program. Gary holds both a B.A.Sc. (8T8) and M.A.Sc. (0T2) in aerospace engineering from the University of Toronto