Invited Talk (Posner Lecture)
Probabilistic Machine Learning: Foundations and Frontiers
Zoubin Ghahramani

Tue Dec 8th 09:00 -- 09:50 AM @ Room 210 AB

Probabilistic modelling provides a mathematical framework for understanding what learning is, and has therefore emerged as one of the principal approaches for designing computer algorithms that learn from data acquired through experience. I will review the foundations of this field, from basics to Bayesian nonparametric models and scalable inference. I will then highlight some current areas of research at the frontiers of machine learning, leading up to topics such as probabilistic programming, Bayesian optimisation, the rational allocation of computational resources, and the Automatic Statistician.

Author Information

Zoubin Ghahramani (University of Cambridge)

Zoubin Ghahramani is Professor of Information Engineering at the University of Cambridge, where he leads the Machine Learning Group. He studied computer science and cognitive science at the University of Pennsylvania, obtained his PhD from MIT in 1995, and was a postdoctoral fellow at the University of Toronto. His academic career includes concurrent appointments as one of the founding members of the Gatsby Computational Neuroscience Unit in London, and as a faculty member of CMU's Machine Learning Department for over 10 years. His current research interests include statistical machine learning, Bayesian nonparametrics, scalable inference, probabilistic programming, and building an automatic statistician. He has held a number of leadership roles as programme and general chair of the leading international conferences in machine learning including: AISTATS (2005), ICML (2007, 2011), and NIPS (2013, 2014). In 2015 he was elected a Fellow of the Royal Society.

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