Explore the world of reinforcement learning (RL) in this comprehensive interview from the GOTO Book Club. Gain insights from Phil Winder, author of "Reinforcement Learning" and CEO of Winder Research, and Rebecca Nugent, Fienberg Professor of Statistics & Data Science at Carnegie Mellon University. Discover key RL concepts, industry applications, and fundamental aspects such as problem definition and getting started with RL. Learn about the differences between RL and machine learning, its applications in education and long-term strategies, and the challenges of interpreting RL results. Delve into the exciting possibilities and real-life applications of RL algorithms, and understand how this technology is shaping business decision-making processes.
Overview
Syllabus
Intro
Introduction of Rebecca & Phil
Reinforcement learning RL vs machine learning ML
Key terminology for RL
RL in education
What kind of industry applications is RL best suited for?
Can we apply RL to long-term strategies?
Are the people running the algorithms, running the company?
RL and people
Problem definition and RL
The book
What excites you the most about RL?
What are the challenges with the interpretability of RL results?
Applying RL algorithms to real life
Thank you and goodbye Rebecca & Phil
Takeaways
Outro
Taught by
GOTO Conferences
