Computers are traditionally viewed as logical machines which follow precise, deterministic instructions. The real world in which they operate, however, is full of complexity, ambiguity, and uncertainty. In this talk, Professor Chris Bishop discusses recent developments in the field of machine learning, including the use of graphical models and the development of fast algorithms for inference and learning, which have greatly expanded the variety and scale of machine learning applications. The key role played by probability theory will be emphasised, and the talk will be illustrated with examples and case studies.
Chris Bishop is the Chief Research Scientist at Microsoft Research Cambridge. He is also Professor of Computer Science at the University of Edinburgh, and a Fellow of Darwin College, Cambridge. He is the author of the leading textbook, Pattern Recognition and Machine Learning (Springer, 2006). His research interests include probabilistic approaches to machine learning, as well as their application to fields such as biomedical sciences and healthcare. In 2008 he presented the 180th Royal Institution Christmas Lectures, with the title Hi-tech Trek: The Quest for the Ultimate Computer, which were broadcast on UK national television.