Machine Learning Class (Session #17)


October 5, 2012


Christopher Bishop




October 5: Modeling Day

Model Based Machine Learning 1: A Gentle Introduction
Chris Bishop

In the traditional approach to problem solving with machine learning, the developer typically selects from amongst the many machine learning algorithms developed over the last few decades, perhaps influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In these lectures we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several advantages, including the opportunity to create highly tailored models for specific scenarios, rapid prototyping and comparison of a range of alternative models, and transparency of functionality. This first lecture on model-based machine learning will introduce the basic concepts of probabilistic models and factor graphs using simple examples, and will show how they can provide the foundation for model-based machine learning. The lecture assumes no previous knowledge of machine learning or probabilities.


Christopher Bishop

Chris Bishop is a Distinguished Scientist and Deputy Managing Director at Microsoft Research Cambridge, where he is head of the Machine Learning and Perception group. His research interests include probabilistic approaches to machine learning, as well as their application to fields such as biomedical sciences and healthcare. He is also Professor of Computer Science at the University of Edinburgh where he is a member of the Institute for Adaptive and Neural Computation in the School of Informatics. He is a Fellow of Darwin College Cambridge, a Fellow of the Royal Academy of Engineering, and a Fellow of the Royal Society of Edinburgh. Chris is the author of the influential textbooks Neural Networks for Pattern Recognition (Oxford University Press, 1995) which has over 23,000 citations, and Pattern Recognition and Machine Learning (Springer, 2006), which has over 16,000 citations. He has an MA in Physics from Oxford, and a PhD in quantum field theory from the University of Edinburgh.