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1 Introduction
2 Supervised Learning 3 Bayesian Decision Theory 4 Parametric Methods 5 Multivariate Methods 6 Dimensionality Reduction 7 Clustering 8 Nonparametric Methods 9 Decision Trees 10 Linear Discrimination 11 Multilayer Perceptrons 12 Deep Learning 13 Local Models 14 Kernel Machines 15 Graphical Models 16 Hidden Markov Models 17 Bayesian Estimation 18 Combining Multiple Learners 19 Reinforcement Learning 20 Design and Analysis of Machine Learning Experiments A Probability B Linear Algebra C Optimization |