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Introduction, mathematical preliminaries; Pattern Recognition basics; Probability Distributions; Linear Models for Regression; Linear Models for Classification; Neural Networks; Kernel Methods; Sparse Kernel Machines; Graphical Models; Mixture Models and EM; Continuous Latent Variables ; Combining Models; Sequential Data; Approximate Inference; Sampling Methods |