The preface and table of contents of the book "Pattern Recognition and Neural Networks" by B. D. Ripley provide an overview of the content, which covers a wide range of topics in pattern recognition and neural networks. The book begins with an introduction to the field, explaining how neural methods differ from traditional approaches and outlining the pattern recognition task. It then delves into statistical decision theory, discussing Bayes rules, parametric models, logistic discrimination, and predictive classification. The text also explores linear discriminant analysis, flexible discriminants, and feed-forward neural networks, including their biological motivations, theoretical foundations, learning algorithms, and Bayesian perspectives. Additionally, the book covers non-parametric methods, tree-structured classifiers, belief networks, and unsupervised methods such as projection methods, multidimensional scaling, clustering algorithms, and self-organizing maps. The latter part of the book addresses finding good pattern features, bounds for the Bayes error, and statistical sidelines like maximum likelihood estimation, the EM algorithm, Markov chain Monte Carlo, and optimization techniques. The book concludes with a glossary, references, author index, and subject index.The preface and table of contents of the book "Pattern Recognition and Neural Networks" by B. D. Ripley provide an overview of the content, which covers a wide range of topics in pattern recognition and neural networks. The book begins with an introduction to the field, explaining how neural methods differ from traditional approaches and outlining the pattern recognition task. It then delves into statistical decision theory, discussing Bayes rules, parametric models, logistic discrimination, and predictive classification. The text also explores linear discriminant analysis, flexible discriminants, and feed-forward neural networks, including their biological motivations, theoretical foundations, learning algorithms, and Bayesian perspectives. Additionally, the book covers non-parametric methods, tree-structured classifiers, belief networks, and unsupervised methods such as projection methods, multidimensional scaling, clustering algorithms, and self-organizing maps. The latter part of the book addresses finding good pattern features, bounds for the Bayes error, and statistical sidelines like maximum likelihood estimation, the EM algorithm, Markov chain Monte Carlo, and optimization techniques. The book concludes with a glossary, references, author index, and subject index.