Charles M. Lieber Department of Chemistry and Chemical Biology Harvard John A. Paulson School of Engineering and Applied Sciences Harvard University 12 Oxford Street, Cambridge, MAPersonal Data

Source URL: cml.harvard.edu

The Automatic Statistician and Future Directions in Probabilistic Machine Learning Zoubin Ghahramani Department of Engineering University of Cambridge

Source URL: mlss.tuebingen.mpg.de

A Nonparametric Bayesian Approach to Modeling Overlapping Clusters Zoubin Ghahramani∗ Engineering Department University of Cambridge

Source URL: mlg.eng.cam.ac.uk

Dr. Richard E. Turner Contact Details Computational and Biological Learning Lab Department of Engineering University of Cambridge, CB2 1PZ, UK

Source URL: learning.eng.cam.ac.uk

Probabilistic Modelling and Bayesian Inference Zoubin Ghahramani Department of Engineering University of Cambridge, UK

Source URL: mlss.tuebingen.mpg.de

Model-based Approaches to Robust Speech Recognition in Diverse Environments Yongqiang Wang Darwin College Engineering Department Cambridge University

Source URL: mi.eng.cam.ac.uk

• Estimation theory
• Signal processing
• Computer accessibility
• Speech recognition
• Linear regression
• Maximum likelihood estimation

Introduction to Octave Dr. P.J.G. Long Department of Engineering University of Cambridge Based on the Tutorial Guide to Matlab written by Dr. Paul Smith

Source URL: www-mdp.eng.cam.ac.uk

• Software
• Mathematical software
• Application software
• Array programming languages
• GNU Octave
• MATLAB
• Array programming
• Trigonometric functions
• Sine
• Logarithm
• Division
• Octave

Contour-Based Learning for Object Detection Jamie Shotton Department of Engineering University of Cambridge

Source URL: jamie.shotton.org

• Artificial intelligence
• Computer vision
• Machine learning
• Feature detection
• Ensemble learning
• Boosting
• Constellation model
• AdaBoost
• Image segmentation
• Edge detection
• Outline of object recognition
• Decision stump

Sequential Monte Carlo for graphical models: Graph decompositions and Divide-and-Conquer SMC Fredrik Lindsten Department of Engineering The University of Cambridge Cambridge, UK

Source URL: users.isy.liu.se

All activities take place in the Cambridge University Engineering Department, Trumpington Street, except where indicated otherwise. Wednesday – 21 July:00 Tutorial I:

Source URL: www.dsm-conference.org