About
Chris Bishop is a Microsoft Distinguished Scientist 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, and in 2007 he was elected Fellow of the Royal Society of Edinburgh
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.
Publications
Artificial intelligence and machine learning
Exposing variation in climate change risk assessment
Matthew Smith, Drew Purves, Lucas Joppa, Stephen Emmott, Vassily Lyutsarev, Christopher Bishop, Paul I. Palmer, Ben Calderhead, Mark Vanderwel, AGU, October 6, 2015, View abstractStudents, Teachers, Exams and MOOCs: Predicting and Optimizing Attainment in Web-Based Education Using a Probabilistic Graphical Model
Bar Shalem, John Guiver, Christopher Bishop, Yoram Bachrach, ECML/PKDD, September 1, 2014, View abstract, Download PDFChanging how Earth System Modelling is done to provide more useful information for decision making, science and society
Matthew Smith, Paul I. Palmer, Drew Purves, Mark Vanderwel, Vassily Lyutsarev, Ben Calderhead, Lucas Joppa, Christopher Bishop, Stephen Emmott, in Bulletin of the American Meteorological Society, American Meteorological Society, February 1, 2014, View abstract, Download PDFArtificial Life
Christopher Bishop, in Life, Cambridge University Press, January 1, 2014, View abstract, Download PDFMultiple Atopy Phenotypes and Their Associations with Asthma: Similar Findings From Two Birth Cohorts
N. Lazic, G. Roberts, A. Custovic, D. Belgrave, Christopher Bishop, John Winn, J.A. Curtin, S. Hasan Arshad, A. Simpson, in Allergy, June 1, 2013, View abstract, Download PDFModel-Based Machine Learning
Christopher Bishop, February 1, 2013, View abstract, Download PDFStructural Expectation Propagation (SEP): Bayesian structure learning for networks with latent variables
Nevena Lazic, C. M. Bishop, J. Winn, Christopher Bishop, John Winn, in Proceedings Sixteenth International Conference on Artificial Intelligence and Statistics (AIStats), AISTATS, January 1, 2013, View abstract, Download PDFBayesian Machine Learning Approaches for Longitudinal Latent Class Modelling to Define Wheezing Phenotypes to Elucidate Genetic and Environmental Predisposition
Danielle Belgrave, Angela Simpson, Iain Buchan, Adnan Custovic, Christopher Bishop, Methods and models for Latent Variables Conference, Naples. Quaderni di Statistica, January 1, 2012, View abstract, Download PDFBroad versus Narrow: Modelling Strategies for Online Behavioural Targeting
Markus Svensén, Qing Xu, David Stern, Steve Hanks, Christopher Bishop, in In Proceedings of the Fifth International Workshop on Data Mining and Audience Intelligence for Advertising (ADKDD), San Diego, USA, ACM Press, August 21, 2011, View abstract, Download PDFA Comparison of Bayesian and Frequentist Methods for Identifying Markers of Susceptibility to Asthma
Danielle Belgrave, Christopher Bishop, Adnan Custovic, Angela Simpson, Aida Semic-Jusufagic, Andrew Pickles, Iain Buchan, in Proceedings of the International Workshop of Statistical Modelling, Valencia, January 1, 2011, View abstract, Download PDFBeyond Atopy: Multiple Patterns of Sensitization in Relation to Asthma in a Birth Cohort Study
A. Simpson, V.Y. Tan, J. Winn, M. Svensen, C.M. Bishop, D.E. Heckerman, I. Buchan, A. Custovic, Markus Svensén, Christopher Bishop, John Winn, David Heckerman, in American Journal of Respiratory and Critical Care Medicine, February 18, 2010, View abstract, Download PDF, View external linkA Unified Modeling Approach to Data-Intensive Healthcare
Iain Buchan, John Winn, Christopher Bishop, in The Fourth Paradigm: Data-Intensive Scientific Discovery, Microsoft Research, January 1, 2009, View abstract, Download PDFA New Framework for Machine Learning
Christopher Bishop, in In computational Intelligence: Research Frontiers, IEEE World Congress on Computational Intelligence, WCCI 2008, Hong Kong, June 2008 Lecture Notes in Computer Science, Springer, June 1, 2008, View abstract, Download PDFGenerative or Discriminative? Getting the Best of Both Worlds
Christopher Bishop, Julia Lasserre, in Bayesian Statistics, Oxford University Press, January 1, 2007, View abstract, Download PDFPrincipled Hybrids of Generative and Discriminative Models
Julia A. Lasserre, Christopher Bishop, Thomas P. Minka, Tom Minka, in IEEE Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, June 17, 2006, View abstract, Download PDFPattern Recognition and Machine Learning
Christopher Bishop, Springer, January 1, 2006, View abstract, View external linkGenerative versus Discriminative Methods for Object Recognition
Ilkay Ulusoy, Christopher Bishop, in In Proceedings IEEE International Conference on Computer Vision and Pattern Recognition, CVPR., San Diego., June 20, 2005, View abstract, Download PDFComparison of Generative and Discriminative Techniques for Object Detection and Classification
Ilkay Ulusoy, Christopher Bishop, in Proceedings Sicily Workshop on Object Recognition, Sicily, January 1, 2005, View abstract, Download PDFObject Recognition via Local Patch Labelling
Christopher Bishop, Ilkay Ulusoy, in Proceedings 2004 Workshop on Machine Learning, Sheffield, Springer, January 1, 2005, View abstract, Download PDFGenerative Models and Bayesian Model Comparison for Shape Recognition
Balaji Krishnapuram, Christopher Bishop, Martin Szummer, in 9th Intl. Workshop on Frontiers in Handwriting Recognition (IWFHR), October 1, 2004, View abstract, Download PDFClumps, Clusters and Classification
Christopher Bishop, in Computer Systems: Theory, Technology and Applications. A Tribute to Roger Needham, Springer, January 1, 2004, View abstract, View external linkDistinguishing text from graphics in on-line handwritten ink
Christopher Bishop, Markus Svensén, Geoffrey E. Hinton, in Proceedings International Workshop on Frontiers in Handwriting Recognition, IWFHR-9, January 1, 2004, View abstract, Download PDFBayesian Regression and Classification
Christopher Bishop, Michael E. Tipping, in Advances in Learning Theory: Methods, Models and Applications, IOS Press, NATO Science Series III: Computer and S, January 1, 2003, View abstract, Download PDFBayesian Image Super-resolution
Michael E. Tipping, Christopher Bishop, in Advances in Neural Information Processing Systems, January 1, 2002, View abstract, Download PDFDiscussion of `Bayesian Treed Generalized Linear Models’ by H. A. Chipman, E. I. George and R. E. McCulloch
Christopher Bishop, in Proceedings Seventh Valencia International Meeting on Bayesian Statistics, Oxford University Press, January 1, 2002, View abstract, Download PDFFeature representation for the automatic analysis of fluorescence in-situ hybridization images
Boaz Lerner, William F. Clocksin, Seema Dhanjal, Maj A. Hulten, Christopher Bishop, in IEEE Transactions on Systems, Man and Cybernetics, January 1, 2001, View abstract, Download PDFVariational Relevance Vector Machines
Christopher Bishop, Michael E Tipping, in Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence; Proceedings of the 15th International Workshop on Statistical Modelling, Morgan Kaufmann, January 1, 2000, View abstract, Download PDFBayesian PCA
Christopher Bishop, in Advances in Neural Information Processing Systems, MIT Press, January 1, 1999, View abstract, Download PDFLatent Variable Models
Christopher Bishop, in Learning in Graphical Models, MIT Press, January 1, 1999, View abstract, Download PDFMixtures of Probabilistic Principal Component Analyzers
M. E. Tipping, Christopher Bishop, in Neural Computation, January 1, 1999, View abstract, Download PDFNeural network training using multi-channel data with aggregate labelling
N. McGrogan, Christopher Bishop, L. Tarassenko, in Proceedings Ninth International Conference on Artificial Neural Networks, ICANN'99, IEE, January 1, 1999, View abstract, Download PDF, View external linkNeural Network-Based Wind Vector Retrieval from Satellite Scatterometer Data
Dan Cornford, Ian T. Nabney, Christopher Bishop, in Neural Computing and Applications, January 1, 1999, View abstract, Download PDFNeural Networks and Machine Learning
Christopher Bishop, Springer Verlag, November 25, 1998, View abstract, View external linkGTM: The Generative Topographic Mapping
Christopher Bishop, Markus Svensén, Christopher K.I. Williams, in Neural Computation, January 1, 1998, View abstract, Download PDFApproximating posterior distributions in belief networks using mixtures
Christopher Bishop, N. Lawrence, T. Jaakkola, M. I. Jordan, in Advances in Neural Information Processing Systems, January 1, 1998, View abstract, Download PDFEnsemble learning for multi-layer networks
D. Barber, Christopher Bishop, in Advances in Neural Information Processing Systems, January 1, 1998, View abstract, Download PDFEnsemble learning in Bayesian neural networks
D. Barber, Christopher Bishop, in Generalization in Neural Networks and Machine Learning, Springer Verlag, January 1, 1998, View abstract, Download PDFMarkovian Inference in Belief Networks
B. J. Frey, N. Lawrence, Christopher Bishop, January 1, 1998, View abstract, Download PDFMixture representations for inference and learning in Boltzmann machines
N. Lawrence, Christopher Bishop, M. Jordan, in Uncertainty in Artificial Intelligence, Morgan Kaufmann, January 1, 1998, View abstract, Download PDFPulsed Neural Networks
Wolfgang Maass, Christopher Bishop, MIT Press, January 1, 1998, View abstract, View external linkRegression with Input-Dependent Noise: A Gaussian Process Treatment
P. W. Goldberg, C. K. I. Williams, Christopher Bishop, in Advances in Neural Information Processing Systems, MIT Press, January 1, 1998, View abstract, Download PDFNeural Networks
Christopher Bishop, in Bullock, A. and Trombley, S. (Eds.) Fontana Dictionary of Modern Thought (Third ed.), Fontana Press, January 1, 1997, View abstract, View external linkNeural Networks
M. I. Jordan, Christopher Bishop, in Tucker, A. B. (Ed.), The Computer Science and Engineering Handbook, CRC Press, January 1, 1997, View abstract, Download PDFAn Upper Bound on the Bayesian Error Bars for Generalized Linear Regression
Cazhaow S. Qazaz, Christopher K. I. Williams, Christopher Bishop, in Ellacott, S. W. Mason, J. C. and Anderson, I. J. (Eds.), Mathematics of Neural Networks: Models, Algorithms and Applications, Kluwer, January 1, 1997, View abstract, Download PDFBayesian Model Comparison by Monte Carlo Chaining
David Barber, Christopher Bishop, in Advances in Neural Information Processing Systems, MIT Press, January 1, 1997, View abstract, Download PDFBayesian neural networks
Christopher Bishop, in Journal of the Brazilian Computer Society, January 1, 1997, View abstract, View external linkLatent Variables, Topographic Mappings and Data Visualization
Christopher Bishop, in Proceedings IX Italian Workshop on Neural Networks, Vietri sur Mare, Salerno, Springer-Verlag, January 1, 1997, View abstract, View external linkModelling Conditional Probability Densities for Periodic Variables
Christopher Bishop, Ian T. Nabney, in Mathematics of Neural Networks: Models, Algorithms and Applications, Kluwer Academic Press, January 1, 1997, View abstract, View external linkRegression with Input-Dependent Noise: A Bayesian Treatment
Christopher Bishop, C. S. Qazaz, in Advances in Neural Information Processing Systems, MIT Press, January 1, 1997, View abstract, Download PDFNeural Networks
M. I. Jordan, Christopher Bishop, in ACM Computing Surveys, January 1, 1996, View abstract, Download PDFEM optimization of latent variable density models
Christopher Bishop, Markus Svensén, C. K. I. Williams, in Advances in Neural Information Processing Systems, MIT Press, January 1, 1996, View abstract, Download PDFModelling conditional probability distributions for periodic variables
Christopher Bishop, I. T. Nabney, in Neural Computation, January 1, 1996, View abstract, View external linkNeural Networks: A Pattern Recognition Perspective
Christopher Bishop, in Handbook of Neural Computation, Oxford University Press and IOP Publishing, January 1, 1996, View abstract, Download PDFMultiphase Flow Monitoring in Oil Pipelines
Christopher Bishop, in Murray, A. F. (Ed.), Applications of Neural Networks, Kluwer, January 1, 1995, View abstract, View external linkModelling Conditional Probability Distributions for Periodic Variables
I. T. Nabney, C. Legleye, Christopher Bishop, in Proceedings Fourth IEE International Conference on Artificial Neural Networks, Cambridge, UK, January 1, 1995, View abstract, Download PDFReal-time control of a tokamak plasma using neural networks
Christopher Bishop, P.S. Haynes, M.E.U. Smith, T.N. Todd, D.L. Trotman, R.C.G. Windsor, in Tesauro, G. Touretzky, D. S. and Leen, T. K. (Eds.) Advances in Neural Information Processing Systems, MIT Press, January 1, 1995, View abstract, Download PDFNeural Networks for Pattern Recognition
Christopher Bishop, Oxford University Press, January 1, 1995, View abstract, View external linkBayesian methods for neural networks
Christopher Bishop, in Technical Report NCRG/95/009, Neural Computing Research Group, Aston University, January 1, 1995, View abstract, Download PDFEstimating conditional probability densities for periodic variables
Christopher Bishop, C. Legleye, in Advances in Neural Information Processing Systems, MIT Press, January 1, 1995, View abstract, Download PDFOn the relationship between Bayesian error bars and the input data density
C. K. I. Williams, C. Qazaz, Christopher Bishop, H. Zhu, in Proceedings Fourth IEE International Conference on Artificial Neural Networks, IEE, January 1, 1995, View abstract, Download PDFReal-time control of a tokamak plasma using neural networks
Christopher Bishop, P. S. Haynes, M. E. U. Smith, T. N. Todd, D. L. Trotman, in Neural Computation, January 1, 1995, View abstract, Download PDFRecent Progress in the Measurement and Analysis of ECE on JET
D Bartlett, C Bishop, R Cahill, A McLachlan, L Porte, A Rookes, Christopher Bishop, in Proceedings of the 9th International Workshop on ECE and ECRH, January 1, 1995, View abstract, Download PDFRegularization and Complexity Control in Feed-forward Networks
Christopher Bishop, in Proceedings International Conference on Artificial Neural Networks ICANN'95, EC2 et Cie, January 1, 1995, View abstract, Download PDFNeural Networks and Their Applications
Christopher Bishop, in Review of Scientific Instruments, American Institute of Physics, June 1, 1994, View abstract, Download PDFFast Feedback Control of a High Temperature Fusion Plasma
Christopher Bishop, Paul S. Haynes, Mike E. U. Smith, Tom N. Todd, David L. Trotman, in Neural Computing and Applications, January 1, 1994, View abstract, Download PDFNovelty Detection and Neural Network Validation
Christopher Bishop, in IEE Proceedings: Vision, Image and Signal Processing. Special issue on applications of neural networks., January 1, 1994, View abstract, Download PDFMixture Density Networks
Christopher Bishop, January 1, 1994, View abstract, Download PDFNeural Network Validation: an Illustration from the Monitoring of Multi-phase Flows
C. M. Bishop, Christopher Bishop, in Proceedings IEE Conference on Artificial Neural Networks, May 1, 1993, View abstract, View external linkNovelty Detection and Neural Network Validation
Christopher Bishop, in Gielen, S. and Kappen, B. (Eds.), Proceedings International Conference on Artificial Neural Networks ICANN'93, January 1, 1993, View abstract, View external linkReconstruction of Tokamak Density Profiles Using Feed-forward Networks
Christopher Bishop, Iain Strachan, John O'Rourke, Geoff Maddison, Paul Thomas, in Neural Computing and Applications, Springer-Verlag, January 1, 1993, View abstract, Download PDFAnalysis of Multiphase Flows Using Dual-energy Gamma Densitometry and Neural Networks
Christopher Bishop, G.D. James, in Nuclear Instruments and Methods in Physics Research, January 1, 1993, View abstract, Download PDFAutomatic analysis of JET charge exchange recombination spectra using neural networks
Christopher Bishop, C. M. Roach, M. G. von Hellermann, in Plasma Physics and Controlled Fusion, January 1, 1993, View abstract, Download PDFCurvature-driven smoothing: a learning algorithm for feedforward networks
Christopher Bishop, in IEEE Transactions on Neural Networks, January 1, 1993, View abstract, Download PDFFast Curve Fitting Using Neural Networks
Christopher Bishop, C.M. Roach, in Review of Scientific Instruments, June 22, 1992, View abstract, Download PDFCurvature-driven Smoothing in Back-propagation Neural Networks
Christopher Bishop, in Taylor, J. G. and Mannion, C. L. T. (Eds.), Theory and Applications of Neural Networks, Springer, January 1, 1992, View abstract, View external linkA Neural Network Approach to Tokamak Equilibrium Control
Christopher Bishop, Peter Cox, Paul S. Haynes, Colin M. Roach, Mike E. U. Smith, Tom N. Todd, David L. Trotman, in Neural Network Applications, Springer, January 1, 1992, View abstract, View external linkExact Calculation of the Hessian Matrix for the Multilayer Perceptron
Christopher Bishop, in Neural Computation, January 1, 1992, View abstract, Download PDFHardware Implementation of a Neural Network for Plasma Position Control in Compass-D
Christopher Bishop, P. S. Haynes, C. M. Roach, T. N. Todd, D. L. Trotman, M. E. U. Smith, in Proceedings of the 17th Symposium on Fusion Technology, Rome, Italy, Elsevier Science Publishers, January 1, 1992, View abstract, View external linkA Fast Procedure for Retraining the Multilayer Perceptron
Christopher Bishop, in International Journal of Neural Systems,, World Scientific Publishing Company, January 1, 1991, View abstract, Download PDFA Fast Procedure for Retraining the Multilayer Perceptron
Christopher Bishop, in International Journal of Neural Systems, January 1, 1991, View abstract, Download PDFImproving the Generalization Properties of Radial Basis Function Neural Networks
Christopher Bishop, in Neural Computation, January 1, 1991, View abstract, View external linkBallooning Delta-prime in the Second Stable Region
Christopher Bishop, R. J. Hastie, A. Sykes, H. R. Wilson, in Physics of Fluids, August 13, 1990, View abstract, Download PDFCurvature-driven Smoothing in Back-propagation Neural Networks
Christopher Bishop, in Angeniol, B. and Widrow, B. (Eds.), International Neural Networks Conference, INNC'90, IEEE, January 1, 1990, View abstract, View external linkAlpha Particle Induced Magnetoacoustic Instability in a Thermonuclear Plasma
Christopher Bishop, R. Fitzpatrick, R. J. Hastie, J. C. Jackson, in Plasma Physics, January 1, 1989, View abstract, Download PDFBifurcated Temperature Profiles and the H-mode
Christopher Bishop, in Nuclear Fusion, January 1, 1986, View abstract, Download PDFMicro-instability Based Models for Confinement Properties and Ignition Criteria in Tokamaks
W.M. Tang, Christopher Bishop, in 11th European Conference on Plasma Physics and Controlled Fusion Research, Kyoto., January 1, 1986, View abstract, Download PDFDegenerate Toroidal Magnetohydrodynamic Equilibria and Minimum B
Christopher Bishop, J. B. Taylor, in Physics of Fluids, January 1, 1986, View abstract, Download PDFStability of Localised MHD Modes in Divertor Tokamaks – a picture of the H-mode
Christopher Bishop, in Nuclear Fusion, January 1, 1986, View abstract, Download PDFStability of Anisotropic Pressure Tokamak Equilibria to Ideal Ballooning Modes
R.J. HASTIE, Christopher Bishop, in Nuclear Fusion, January 1, 1985, View abstract, Download PDFIdeal MHD Ballooning Stability in the Vicinity of a Separatrix
Christopher Bishop, P. Kirby, J. W. Connor, R. J. Hastie, J. B. Taylor, in Nuclear Fusion, January 1, 1984, View abstract, View external linkTopological Charge Distribution in SU(N) Gauge Theories
Christopher Bishop, P. V. D. Swift, in Physics Letters, January 1, 1983, View abstract, View external linkMathematics
Exposing variation in climate change risk assessment
Matthew Smith, Drew Purves, Lucas Joppa, Stephen Emmott, Vassily Lyutsarev, Christopher Bishop, Paul I. Palmer, Ben Calderhead, Mark Vanderwel, AGU, October 6, 2015, View abstractChanging how Earth System Modelling is done to provide more useful information for decision making, science and society
Matthew Smith, Paul I. Palmer, Drew Purves, Mark Vanderwel, Vassily Lyutsarev, Ben Calderhead, Lucas Joppa, Christopher Bishop, Stephen Emmott, in Bulletin of the American Meteorological Society, American Meteorological Society, February 1, 2014, View abstract, Download PDFOn the Difficulty of Determining Tearing Mode Stability
Christopher Bishop, J. W. Connor, R. J. Hastie, S. C. Cowley, in Plasma Physics, January 1, 1991, View abstract, Download PDFData visualization, analytics, and platform
The Study Team for Early Life Asthma Research (STELAR) consortium ‘Asthma e-lab’: team science bringing data, methods and investigators together
Adnan Custovic, John Ainsworth , Hasan Arshad , Christopher Bishop, Iain Buchan, Paul Cullinan , Graham Devereux , John Henderson , John Holloway , Graham Roberts , Steve Turner , Ashley Woodcock , Angela Simpson , in Thorax, BMJ Publishing Group Ltd, March 24, 2015, View abstract, Download PDF, View external linkA Hierarchical Latent Variable Model for Data Visualization
Christopher Bishop, M. E. Tipping, in IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE, January 1, 1998, View abstract, Download PDFMedical, health and genomics
Developmental Profiles of Eczema, Wheeze, and Rhinitis: Two Population-Based Birth Cohort Studies
Danielle C. M. Belgrave, Raquel Graneli, Angela Simpson, John Guiver, Christopher Bishop, Iain Buchan, A. John Henderson, Adnan Custovic, in PLOS Medicine, Public Library of Science, October 1, 2014, View abstract, Download PDFTrajectories of Lung Function during Childhood
Danielle C. M. Belgrave, Iain Buchan, Christopher Bishop, Lesley Lowe, Angela Simpson, Adnan Custovic, in American Journal of Respiratory and Critical Care Medicine, January 5, 2014, View abstract, Download PDFA Unified Modeling Approach to Data-Intensive Healthcare
Iain Buchan, John Winn, Christopher Bishop, in The Fourth Paradigm: Data-Intensive Scientific Discovery, Microsoft Research, January 1, 2009, View abstract, Download PDFAutomatic signal classification in fluorescence in-situ hybridization images
B. Lerner, W. F. Clocksin, S. Dhanjal, M. A. Hulten, Christopher Bishop, in Cytometry, January 1, 2001, View abstract, Download PDFAn Investigation of Coupled Energy and Particle Transport in Tokamak Plasmas
N. Deliyanakis, Christopher Bishop, J. W. Connor, M. Cox, D. C. Robinson, in Plasma Physics and Controlled Fusion, January 1, 1994, View abstract, Download PDFProgramming languages and software engineering
Changing how Earth System Modelling is done to provide more useful information for decision making, science and society
Matthew Smith, Paul I. Palmer, Drew Purves, Mark Vanderwel, Vassily Lyutsarev, Ben Calderhead, Lucas Joppa, Christopher Bishop, Stephen Emmott, in Bulletin of the American Meteorological Society, American Meteorological Society, February 1, 2014, View abstract, Download PDFDiscriminative Writer Adaptation
Martin Szummer, Christopher Bishop, in 10th Intl. Workshop on Frontiers in Handwriting Recognition (IWFHR), October 1, 2006, View abstract, Download PDFComputer vision
Bayesian Hierarchical Mixtures of Experts
Christopher Bishop, Markus Svensén, in Proceedings Nineteenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, January 1, 2003, View abstract, Download PDFComputer systems and networking
Optimising Synchronisation Times for Mobile Devices
Neil D. Lawrence, Antony "Ant" Rowstron, Christopher Bishop, Mike Taylor, in Advances in Neural Information Processing Systems, MIT Press, January 1, 2001, View abstract, Download PDFNon-linear Bayesian image modelling
Christopher Bishop, John Winn, in Proceedings Sixth European Conference on Computer Vision, Springer-Verlag, January 1, 2000, View abstract, Download PDFPattern Recognition and Feedforward Neural Networks
Christopher Bishop, in The MIT Encyclopedia of the Cognitive Sciences, MIT Press, January 1, 1999, View abstract, Download PDFHeat-Pulse Propagation in Tokamaks and the Role of Density Perturbations
Christopher Bishop, J. W. Connor, in Plasma Physics, January 1, 1990, View abstract, Download PDFAlgorithms
Mixtures of Probabilistic Principal Component Analyzers
M. E. Tipping, Christopher Bishop, in Neural Computation, January 1, 1999, View abstract, Download PDFDevelopments of the Generative Topographic Mapping
Christopher Bishop, Markus Svensén, Christopher K. I. Williams, in Neurocomputing, January 1, 1998, View abstract, Download PDFBayesian Inference of Noise Levels in Regression
Christopher Bishop, C. S. Qazaz, in Proceedings 1996 International Conference on Artificial Neural Networks, ICANN'96, Bochum, Germany, Springer-Verlag, January 1, 1997, View abstract, Download PDFGTM through time
Christopher Bishop, Geoffrey E. Hinton, Iain G. D. Strachan, in Proceedings IEE Fifth International Conference on Artificial Neural Networks, Cambridge, U.K., January 1, 1997, View abstract, Download PDFGTM: a principled alternative to the Self-Organizing Map
Christopher Bishop, Markus Svensén, Christopher K. I. Williams, in International Conference on Artificial Neural Networks, ICANN'96, Springer, January 1, 1997, View abstract, Download PDFMagnification factors for the GTM algorithm
Christopher Bishop, Markus Svensén, Christopher K. I. Williams, in Proceedings IEE Fifth International Conference on Artificial Neural Networks, Cambridge, U.K., January 1, 1997, View abstract, Download PDFCurvature-driven smoothing: a learning algorithm for feedforward networks
Christopher Bishop, in IEEE Transactions on Neural Networks, January 1, 1993, View abstract, Download PDFNatural language processing and speech
Neural Network Approach to Energy Confinement Scaling in Tokamaks
Leslie Alan, Christopher Bishop, in Plasma Physics and Controlled Fusion, January 1, 1992, View abstract, Download PDFHardware, devices and quantum computing
An Intelligent Shell for the Toroidal Pinch
Christopher Bishop, in Plasma Physics, January 1, 1989, View abstract, Download PDFProjects
Link description
Panel: Progress in AI: Myths, Realities, and Aspirations
Date
July 10, 2015
Speakers
Christopher Bishop, Eric Horvitz, Fei Fei Li, Josh Tenenbaum, Michael L. Littman, and Oren Etzioni
Affiliation
Microsoft Research, Allen Institute for Artificial Intelligence, Stanford University, Brown University, Massachusetts Institute of Technology
Link description
Q and A – Session 1
Date
February 13, 2015
Speakers
Jennifer Chayes, P. Anandan, Rico Malvar, Sriram Rajamani, Christopher Bishop, Victor Bahl, Raj Reddy, Ed Lazowska, and Chandu Thekkath
Affiliation
Microsoft, Carnegie Mellon University, University of Washington
Link description
Panel Discussion: How to do good research and have a successful career in Research
Date
February 13, 2015
Speakers
Jennifer Chayes, P. Anandan, Rico Malvar, Sriram Rajamani, Christopher Bishop, and Victor Bahl
Affiliation
Microsoft
Link description
Computing with Uncertainty
Date
September 26, 2013
Speakers
Christopher Bishop
Affiliation
MSRC
Other
Pattern Recognition and Machine Learning
This leading textbook provides a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. This is the first machine learning textbook to include a comprehensive coverage of recent developments such as probabilistic graphical models and deterministic inference methods, and to emphasize a modern Bayesian perspective. It is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. This hard cover book has 738 pages in full colour, and there are 431 graded exercises (with solutions available below). Extensive support is provided for course instructors.
To view inside this book go to Amazon.
Available from
Support for course tutors
- A complete set of solutions to all exercises, including non-WWW exercises is available to course tutors from Springer.
- Slides for Chapter 1 (Introduction) in PDF, PowerPoint, and PowerPoint 2007 formats.
- Slides for Chapter 2 (Probability Distributions) in PDF, PowerPoint, and PowerPoint 2007 formats.
- Slides for Chapter 3 (Linear Models for Regression) in PDF, PowerPoint, and PowerPoint 2007
- Slides for Chapter 8 (Graphical Models) in PDF, PowerPoint, and PowerPoint 2007 formats.
Downloads
- Contents list and sample chapter (Chapter 8: Graphical Models) in PDF format.
- Solutions manual for the www exercises in PDF format (version: 8 September, 2009).
- Complete set of Figures in JPEG, PNG, PDF and EPS formats, see below.
- A PDF file of errata. There are three versions of this. To determine which one to download, look at the bottom of the page opposite the dedication photograph in your copy of the book. If it says “corrected …2009” then download Version 3. If it says “corrected …2007” then download Version 2. Otherwise download Version 1.
- The book has been translated into Japanese in two volumes. Volume 1 contains chapters 1-5 plus the appendices, while Volume 2 contains chapters 6-14. Support for the Japanese edition is available from here.
- A third party Matlab implementation of many of the algorithms in the book. I’ve not tried this myself and cannot comment on the quality.
Figures
Below are all of the figures from Pattern Recognition and Machine Learning (except for the photographs in Figures 4.8 and A.4). Copyright in these figures is owned by Christopher M. Bishop. Permission is hereby given to download and reproduce the figures for non-commercial purposes including education and research, provided the source of the figures is acknowledged.
I am very grateful to Markus Svensén who has prepared these figures.
The figures are available in JPG, PNG, PDF and EPS formats. Please note that many of the EPS figures have been created using MetaPost, which give them special properties, as described below.
All figures are available in single zipped folders, one for each format.
The EPS figures
Many of the EPS figures have been created using MetaPost. These figures, which are marked (MP) in the table below, are suitable for inclusion in LaTeX documents that are ultimately rendered as postscript documents (or PDF documents produced from postscript, e.g., using Ghostscript or Acrobat Distiller). However, they are not suitable for inclusion in other types of documents, nor can they be viewed on screen using postscript screen viewers such as Ghostview; this usually also affects DVI screen viewers.
Almost all other EPS figures have been produced using Matlab. Several of these contains LaTeX fonts and this confuses postscript screen viewers such as Ghostview, to which the EPS figure appears to be missing its bounding box. However, these figures will still display on screen and the bounding box will be picked up correctly when these figures are used in LaTeX.