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Introduction Probability Distributions Linear Models for Regression Linear Models for Classification Neural Networks Kernel Methods Sparse Kernel Machines Graphical Models Mixture Models and EM Approximate Inference Sampling Methods Continuous Latent Variables Sequential Data Combining Models Appendix Data Sets Probability Distributions Properties of Matrices Calculus of Variations Lagrange Multipliers Pattern Recognition and Machine Learning. Christopher M. Bishop. www.microsoft.com . Feb 1, 2006.