Frontiers of Probabilistic Machine Learning

Abstract: 

Probability theory provides a mathematical framework for understanding learning and for building rational intelligent systems. I will review the foundations and rationale for probabilistic AI. I will then highlight some areas of research interest, touching on Bayesian deep learning, probabilistic programming, Bayesian optimisation, deep sum-product networks, and AI for data science.

Bio: 

Zoubin Ghahramani is Chief Scientist of Uber and a world leader in the field of machine learning, significantly advancing the state-of-the-art in algorithms that can learn from data. He is known in particular for fundamental contributions to probabilistic modeling and Bayesian approaches to machine learning systems and AI. Zoubin also maintains his roles as Professor of Information Engineering at the University of Cambridge and Deputy Director of the Leverhulme Centre for the Future of Intelligence. He was one of the founding directors of the Alan Turing Institute (the UK's national institute for Data Science and AI), and is a Fellow of St John's College Cambridge and of the Royal Society.

Open Data Science

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