Please join us for the next IACS Seminar on Friday, April 13, 2012.
Speaker: Ryan P. Adams, Assistant Professor of Computer Science, SEAS
Location: Maxwell-Dworkin G125, 33 Oxford Street, Cambridge, MA 02138
Time: Informal lunch with speaker, 12:30pm. Talk, 1:00pm.
Title: Markov Chain Monte Carlo for Machine Learning
Abstract:
Probabilistic models are an extremely flexible and powerful way to express dependencies in
data. Machine learning makes wide use of them in discovering latent representations that
are useful for inference and prediction. A continuing challenge, however, is manipulation
of the Bayesian posterior
distribution that results when one applies these models to data. In this talk, I will
give an overview of how Markov chain Monte Carlo (MCMC) helps us tackle these problems and
discuss my ongoing research to make MCMC practical and efficient.
Bio:
In July 2011 Ryan P. Adams was appointed as an Assistant Professor of Computer Science at
the Harvard School of Engineering and Applied Sciences. Previously, he was a
CIFAR<http://www.cifar.ca> Junior Research Fellow at the University of Toronto. His
research focuses on machine learning and computational statistics, but he is broadly
interested in questions related to artificial intelligence, computational neuroscience,
machine vision, and Bayesian nonparametrics.
For information about future events at IACS, see
http://iacs.seas.harvard.edu/events
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