Public policy and deep reinforcement learning on AWS
If you’ve ever wondered if you could use AI to inform public policy, join Emily Webber as she combines classic economic methods with AI techniques to train a reinforcement learning agent on decades of randomized control trials. You’ll learn about classic philosophical foundations for public policy decision making and how these can be applied to solve the problems that impact the many.
Amazon Web Services
Emily Webber is a machine learning specialist solutions architect at Amazon Web Services (AWS). She guides customers from project ideation to full deployment, focusing on Amazon SageMaker, where her customers are household names across the world, such as T-Mobile. She’s been leading data science projects for many years, piloting the application of machine learning into such diverse areas as social media violence detection, economic policy evaluation, computer vision, reinforcement learning, the IoT, drones, and robotic design. Previously, she was a data scientist at the Federal Reserve Bank of Chicago and a solutions architect for an explainable AI startup in Chicago. Her master’s degree is from the University of Chicago, where she developed new applications of machine learning for public policy research with the Data Science for Social Good Fellowship.
For conference registration information and customer service
For more information on community discounts and trade opportunities with O’Reilly conferences
For information on exhibiting or sponsoring a conference
For media/analyst press inquires