Daniel Lee

 

 


 

Dan's research focuses on applying knowledge about biological information processing systems to building better artificial sensorimotor systems that can adapt and learn from experience. Drawing from the ways in which biological systems compute and learn, Dan and his lab look at computational neuroscience models, theoretical foundations of machine learning algorithms, as well as constructing real-time intelligent robotic systems, with an ultimate goal of making machines that better understand what we want them to do.




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Nov 28th 2016

How can robots determine their state and properties of the surrounding environment from noisy sensor measurements in time? In this module you will learn how to get robots to incorporate uncertainty into estimating and learning from a dynamic and changing world. Specific topics that will be covered include probabilistic generative models, Bayesian filtering for localization and mapping.

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