Join us to learn from one of the masters of Reinforcement Learning in our Masterclass with Dr. Matthew Taylor! Part 2 kicks off Monday, Mar 29th and involves a one-hour lectures with plenty of time of Q&A!

Paid event, please register below on eventbrite ($40 CAD for the 2nd class)

REGISTER HERE
https://www.eventbrite.ca/e/reinforcement-learning-masterclass-with-dr-matthew-taylor-2-parts-tickets-144251077857

1) Lecture 1: Reinforcement Learning in the Real World
Date: March 22nd, 12:30pm ET

Reinforcement learning (RL) has had many successes, from AlphaGo to StarCraft 2 and data center cooling. Unfortunately, many real-world business tasks are not easily defined and can be difficult to formulate so that RL can successfully solve them. Furthermore significant amounts of time and/or data may be required to reach acceptable performance, potentially making learning infeasibly slow or expensive. This talk will:

-Briefly remind the audience what RL is;
-Discuss how to effectively identify and formulate an RL problem, including concrete use cases and;
-Identify ways to leverage existing knowledge from other agents, programs, or people to help RL agents to improve their performance.

2) Lecture 2: Centaurs and Augmented Intelligence: Human-AI Systems in 2021
Date: March 29th, 12:30pm ET

Would you want a computer to decide whether you needed an ambulance when you called 911? For the foreseeable future, machine learning and artificial intelligence systems will need to incorporate humans in high-stakes environments. Furthermore, even if your end goal is a completely autonomous solution, it may be much easier to have an initial deployment where the AI is assisting a human. This talk will discuss:

-When and why human-AI systems can outperform human-only or AI-only approaches by leveraging the unique abilities of both humans and AIs,
-State of the art methods for successful combinations, and
-Showcase successful examples to help you identify potential centaur approaches in your business.

Dr. Matthew Taylor
Matthew E. Taylor received his doctorate from the University of Texas at Austin in the summer of 2008, supervised by Peter Stone. He then completed a two-year postdoctoral research position at the University of Southern California with Milind Tambe and spent 2.5 years as an assistant professor at Lafayette College. He was then an assistant professor at Washington State University where he held the Allred Distinguished Professorship in Artificial Intelligence. In 2017, he temporarily left academia to help start an artificial intelligence lab in Edmonton, Alberta, with Borealis AI, the artificial intelligence research lab for the Royal Bank of Canada, where, among other things, he contributed to a deployed reinforcement learning agent for stock trading.