The Nature Of Statistical Learning Theory Djvu File
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Protein sequence modelling and Density Networks ch_learning.ps.gz. abstract. ch_learning.ps.gz. PDF DJVU abstract. ` Bayesian Neural Networks and Density Networks '. density.ps.gz, PDF DJVU abstract ps mirror, Canada `Density Networks and their application to Protein Modelling' (this version appeared in Maximum Entropy Proceedings) density97.ps.gz. PDF DJVU abstract. density97.ps.gz. abstract. D. MacKay and M. Gibbs (1997): ` Density Networks '. (this version in proceedings of Edinburgh meeting, ed. Jim Kay) Modelling of images and radar nn_im_decon.ps.gz, PDF DJVU abstract ps mirror, Canada `Neural Network Image Deconvolution',byJohn E. Tansley, Martin J. Oldfield and David J.C. MacKay radar3.ps.gz. PDF DJVU abstract ps mirror, Canada `Bayesian analysis of linear phased-array radar' by A.G. Green and D.J.C. MacKay. i3.ps.gz (494K). PDF DJVU abstract ps mirror, Canada A. Barnett and D. MacKay: `Bayesian Comparison of Models for Images'. Neuroscience Data Analysis newint.ps.gz. PDF DJVU abstract. newint.ps.gz. abstract.D. MacKay and R. Takeuchi: `Interpolation models with multiple hyperparameters'. (Published in Statistics and Computing) The spatial arrangement of cones in the fovea: Bayesian analysis (unpublished research note, 25.3.1993, discussing dataof Mollon and Bowmaker - Are cones arranged randomly in the retina) (7 pages) cones.pscones.pdf shuffle.ps.gz. PDF DJVU abstract. shuffle.ps.gz. abstract. ` A Comment on Data Shuffling '. by David J. C. MacKay, Christopher deCharms and Virginia R. de Sa. cnssumm.ps.gz. PDF DJVU Ginny's site Old draft: bridge.ps.gz. abstract. bridge.ps.gz. abstract. ` Model fitting as an Aid to Capacitance Compensation and Bridge Balancing in Neuronal Recording '. by David J. C. MacKay and Virginia R. de Sa. - Publication details: de Sa, V.R., & MacKay, D.J.C. (2001). Model fitting as an Aid to Bridge Balancing in Neuronal Recording. Neurocomputing (special issue devoted to Proceedings of the CNS 2000 meeting) Vol38-40, 1651-1656. Time-warp-invariant computation with action potentials: Deductions about the Hopfield-Brody Mouse by David MacKay and Seb WillsIn December 2000, the Inference Group won Hopfield and Brody's `mouse brain' competition. Dynamical Neural Networks dynet.ps.gz. PDF DJVU dynet.ps.gz. Abstract (in Germany) ` A Recurrent Neural Network for Modelling Dynamical Systems '.by Coryn A.L. Bailer-Jones, David J.C. MacKay, Philip J. Withers Papers for which postscript files are not available here, but may be available in Materials ScienceH.K.D.H. Bhadeshia, D.J.C. MacKay, and L.E. Svensson. Impact toughness of C-MN steel arc welds - Bayesian neural network analysis. Materials Science and Technology, 11(10):1046-1051, 1995.
First, it covers supervised learning, discussing decision trees, regression and classification, and neural networks. Then, it covers unsupervised learning, discussing clustering, feature selection, and randomized optimization. Finally, it covers reinforcement learning, discussing markov decision processes, game theory, and decision making.
This course is offered by Google on their developer platform. While most of the courses in this ranking are academic in nature and rather long, this one fits squarely into the category of hands-on introductions to machine learning. 153554b96e