Presented By O'Reilly and Cloudera
Make Data Work
September 25–26, 2017: Training
September 26–28, 2017: Tutorials & Conference
New York, NY

Bayesian machine learning: Quantifying uncertainty and robustness at scale

Machine Learning & Data Science
Location: 1A 06/07 Level: Intermediate
Secondary topics:  Hardcore Data Science
Average rating: ****.
(4.50, 6 ratings)

Tamara Broderick demonstrates new advances in computation for Bayesian machine learning that allow reliable quantification of uncertainty and robustness at modern data scales, illustrated with examples in microcredit and online advertising.

Photo of Tamara Broderick

Tamara Broderick

MIT

Tamara Broderick is the ITT Career Development Assistant Professor in the Department of Electrical Engineering and Computer Science at MIT. Tamara’s recent research is focused on developing and analyzing models for scalable Bayesian machine learning, especially Bayesian nonparametrics. She is a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), the MIT Statistics and Data Science Center, and the Institute for Data, Systems, and Society (IDSS). Tamara has been awarded a Google faculty research award, the ISBA Lifetime Members Junior Researcher Award, the Savage Award (for an outstanding doctoral dissertation in Bayesian theory and methods), the Evelyn Fix Memorial Medal and Citation (for the PhD student on the Berkeley campus showing the greatest promise in statistical research), the Berkeley fellowship, an NSF Graduate Research Fellowship, a Marshall Scholarship, and the Phi Beta Kappa Prize (for the graduating Princeton senior with the highest academic average). She holds a PhD in statistics from the University of California, Berkeley, completed under Michael I. Jordan, an AB in mathematics from Princeton University, a master of advanced study for completion of Part III of the Mathematical Tripos from the University of Cambridge, an MPhil by research in physics from the University of Cambridge, and an MS in computer science from the University of California, Berkeley.

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