Probabilistic Models and Machine Learning


February 27, 2014


The last forty years of the digital revolution has been driven by one simple fact: the number of transistors on a silicon chip doubles every couple of years. Today we are witnessing a second form of exponential growth: in the quantity of data being collected and stored. It is driving a transformation in information technology, from solutions that are explicitly hand-crafted to those which are learned from data. Real-world data, however, is full of complexity, ambiguity and uncertainty and so the data revolution is driving a corresponding transformation from computing with logic to computing with probabilities. This talk will introduce the key ideas of computing with uncertainty, and will be illustrated with tutorial examples and real-world case studies.


Chris Bishop

Chris Bishop has a degree in physics and a PhD in quantum field theory, and worked on the theory of magnetically confined plasmas for the fusion programme for eight years before becoming interested in machine learning. He joined Microsoft Research in 1997 when the new MSR lab in Cambridge was opened, where he is now a Distinguished Scientists and head of the Machine Learning and Perception group. Chris is a Fellow of the Royal Academy of Engineering and a Fellow of the Royal Society of Edinburgh. He is also a Professor of Computer Science at the University of Edinburgh, and is the Vice President of the Royal Institution of Great Britain. Chris is the author of the widely adopted text books Neural Networks for Pattern Recognition (Oxford, 1995), and Pattern Recognition and Machine Learning (Springer, 2005).