Contents -- Series Foreword -- Preface -- 1 Introduction -- 2 Machine-Learning Foundations: The Probabilistic Framework -- 3 Probabilistic Modeling and Inference: Examples -- 4 Machine Learning Algorithms -- 5 Neural Networks: The Theory -- 6 Neural Networks: Applications -- 7 Hidden Markov Models: The Theory -- 8 Hidden Markov Models: Applications -- 9 Probabilistic Graphical Models in Bioinformatics -- 10 Probabilistic Models of Evolution: Phylogenetic Trees -- 11 Stochastic Grammars and Linguistics -- 12 Microarrays and Gene Expression -- 13 Internet Resources and Public Databases -- A Statistics -- B Information Theory, Entropy, and Relative Entropy -- C Probabilistic Graphical Models -- D HMM Technicalities, Scaling, Periodic Architectures, State Functions, and Dirichlet Mixtures -- E Gaussian Processes, Kernel Methods, and Support Vector Machines -- F Symbols and Abbreviations -- References -- Index.