Master Class: Sham Kakade (3-5 November 2014)

Sham Kakade is a principal research scientist at Microsoft Research, New England, a lab in Cambridge, MA. Previously, he was an associate professor at the Department of Statistics, Wharton, University of Pennsylvania (from 2010-2012), and was an assistant professor at the Toyota Technological Institute at Chicago. Sham did a postdoc in the Computer and Information Science department at the University of Pennsylvania under the supervision of Michael Kearns, and completed his PhD at the Gatsby Unit where his advisor was Peter Dayan.

The focus of Sham's work is on designing (and implementing) both statistically and computationally efficient algorithms for machine learning, statistics, and artificial intelligence.

You can view videos of the 3 lectures at the following links:

Tensor Decompositions for Learning Latent Variable Models (Part 1)
Tensor Decompositions for Learning Latent Variable Models (Part 2)
How much computation is required in order to achieve statistical efficiency?

CSML Master Classes are sponsored by Google Deepmind.

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Previous Events

DateDescription
Wed, 05 Nov 2014 How much computation is required in order to achieve statistical efficiency?
Tue, 04 Nov 2014 Tensor Decompositions for Learning Latent Variable Models (Part 2)
Mon, 03 Nov 2014 Tensor Decompositions for Learning Latent Variable Models (Part 1)