Presented By O’Reilly and Intel AI
Put AI to work
Sep 4-5, 2018: Training
Sep 5-7, 2018: Tutorials & Conference
San Francisco, CA
Sergey Levine

Sergey Levine
Professor, UC Berkeley

Sergey Levine is a professor in the Department of Electrical Engineering and Computer Sciences at UC Berkeley. His research focuses on machine learning for decision making and control, with an emphasis on deep learning and reinforcement learning algorithms, and includes developing algorithms for end-to-end training of deep neural network policies that combine perception and control, scalable algorithms for inverse reinforcement learning, deep reinforcement learning algorithms, and more. Applications of this work include autonomous robots and vehicles and computer vision and graphics. Sergey’s work has been featured in many popular press outlets, including the New York Times, the BBC, MIT Technology Review, and Bloomberg Business. Sergey holds a BS, MS, and PhD in computer science, all from Stanford University.

Sessions

2:35pm-3:15pm Friday, September 7, 2018
Deep reinforcement and meta-learning: Building flexible and adaptable machine intelligence
Location: Imperial A
Secondary topics:  Reinforcement Learning
Sergey Levine (UC Berkeley)
Average rating: ****.
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Sergey Levine shares techniques in reinforcement learning that allow you to tackle sequential decision-making problems that arise across a range of real-world deployments of artificial intelligence systems and explains how emerging technologies in meta-learning make it possible for deep learning systems to learn from even small amounts of data. Read more.