Model-Based Machine Learning
Christopher Bishop, Technical Fellow and Laboratory Director, Microsoft Research Cambridge
Christopher Bishop
Technical Fellow and Laboratory Director, Microsoft Research Cambridge
Model-Based Machine Learning
Abstract:
Have you ever felt overwhelmed by the sheer number and variety of different machine learning techniques? Over the last five decades, researchers have created literally thousands of machine learning algorithms, with many new ideas published each year. An engineer wanting to solve a problem using machine learning must choose one or more of these algorithms to try, and their choice is often constrained by those algorithms they happen to be familiar with. In this talk we introduce a new perspective called ‘model-based machine learning’, which recognises the fundamental role played by prior knowledge in all machine learning applications. It provides a compass to guide both newcomers and experts through the labyrinth of machine learning techniques, enabling the formulation and tuning of the appropriate algorithm for each application. We also show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning. Finally, we describe several large-scale commercial applications of this framework.
Bio:
Chris Bishop is a Microsoft Technical Fellow and the Laboratory Director at Microsoft Research Cambridge. He is also Professor of Computer Science at the University of Edinburgh, and a Fellow of Darwin College, Cambridge. In 2004, he was elected Fellow of the Royal Academy of Engineering, in 2007 he was elected Fellow of the Royal Society of Edinburgh and in 2017 he was elected as a Fellow of the Royal Society.
Chris obtained a BA in Physics from Oxford, and a PhD in Theoretical Physics from the University of Edinburgh, with a thesis on quantum field theory. He then joined Culham Laboratory where he worked on the theory of magnetically confined plasmas as part of the European controlled fusion programme.
From there, he developed an interest in pattern recognition, and became Head of the Applied Neurocomputing Centre at AEA Technology. He was subsequently elected to a Chair in the Department of Computer Science and Applied Mathematics at Aston University, where he led the Neural Computing Research Group. Chris then took a sabbatical during which time he was principal organiser of the six month international research programme on Neural Networks and Machine Learning at the Isaac Newton Institute for Mathematical Sciences in Cambridge, which ran in 1997.
After completion of the Newton Institute programme Chris joined the Microsoft Research Laboratory in Cambridge.
Technical Fellow and Laboratory Director, Microsoft Research Cambridge
Model-Based Machine Learning
Abstract:
Have you ever felt overwhelmed by the sheer number and variety of different machine learning techniques? Over the last five decades, researchers have created literally thousands of machine learning algorithms, with many new ideas published each year. An engineer wanting to solve a problem using machine learning must choose one or more of these algorithms to try, and their choice is often constrained by those algorithms they happen to be familiar with. In this talk we introduce a new perspective called ‘model-based machine learning’, which recognises the fundamental role played by prior knowledge in all machine learning applications. It provides a compass to guide both newcomers and experts through the labyrinth of machine learning techniques, enabling the formulation and tuning of the appropriate algorithm for each application. We also show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning. Finally, we describe several large-scale commercial applications of this framework.
Bio:
Chris Bishop is a Microsoft Technical Fellow and the Laboratory Director at Microsoft Research Cambridge. He is also Professor of Computer Science at the University of Edinburgh, and a Fellow of Darwin College, Cambridge. In 2004, he was elected Fellow of the Royal Academy of Engineering, in 2007 he was elected Fellow of the Royal Society of Edinburgh and in 2017 he was elected as a Fellow of the Royal Society.
Chris obtained a BA in Physics from Oxford, and a PhD in Theoretical Physics from the University of Edinburgh, with a thesis on quantum field theory. He then joined Culham Laboratory where he worked on the theory of magnetically confined plasmas as part of the European controlled fusion programme.
From there, he developed an interest in pattern recognition, and became Head of the Applied Neurocomputing Centre at AEA Technology. He was subsequently elected to a Chair in the Department of Computer Science and Applied Mathematics at Aston University, where he led the Neural Computing Research Group. Chris then took a sabbatical during which time he was principal organiser of the six month international research programme on Neural Networks and Machine Learning at the Isaac Newton Institute for Mathematical Sciences in Cambridge, which ran in 1997.
After completion of the Newton Institute programme Chris joined the Microsoft Research Laboratory in Cambridge.