This book, "Neural Networks for Pattern Recognition" by Christopher M. Bishop, provides a comprehensive overview of neural networks and their applications in pattern recognition. It is structured into ten chapters, each covering different aspects of neural networks and related statistical methods. The first chapter introduces statistical pattern recognition, including examples like character recognition, classification, regression, and the challenges of high-dimensional data. The second chapter discusses probability density estimation, covering parametric and non-parametric methods, mixture models, and Bayesian inference. The third chapter focuses on single-layer networks, including linear discriminant functions, perceptrons, and Fisher's linear discriminant. The fourth chapter delves into multi-layer perceptrons, discussing feed-forward networks, threshold and sigmoidal units, error back-propagation, and the Jacobian and Hessian matrices. The fifth chapter explores radial basis functions, including their use in interpolation, regularization, and classification. The sixth chapter covers various error functions, including sum-of-squares, cross-entropy, and entropy. The seventh chapter discusses parameter optimization algorithms, such as gradient descent, conjugate gradients, and Newton's method. The eighth chapter addresses pre-processing and feature extraction, including normalization, missing data, and principal component analysis. The ninth chapter covers learning and generalization, including bias-variance trade-off, regularization, and model order selection. The tenth chapter explores Bayesian techniques, including Bayesian learning, model comparison, and Monte Carlo methods. The book also includes appendices on symmetric matrices, Gaussian integrals, Lagrange multipliers, calculus of variations, and principal components, along with references and an index. The text is suitable for students and researchers in computer science, statistics, and machine learning.This book, "Neural Networks for Pattern Recognition" by Christopher M. Bishop, provides a comprehensive overview of neural networks and their applications in pattern recognition. It is structured into ten chapters, each covering different aspects of neural networks and related statistical methods. The first chapter introduces statistical pattern recognition, including examples like character recognition, classification, regression, and the challenges of high-dimensional data. The second chapter discusses probability density estimation, covering parametric and non-parametric methods, mixture models, and Bayesian inference. The third chapter focuses on single-layer networks, including linear discriminant functions, perceptrons, and Fisher's linear discriminant. The fourth chapter delves into multi-layer perceptrons, discussing feed-forward networks, threshold and sigmoidal units, error back-propagation, and the Jacobian and Hessian matrices. The fifth chapter explores radial basis functions, including their use in interpolation, regularization, and classification. The sixth chapter covers various error functions, including sum-of-squares, cross-entropy, and entropy. The seventh chapter discusses parameter optimization algorithms, such as gradient descent, conjugate gradients, and Newton's method. The eighth chapter addresses pre-processing and feature extraction, including normalization, missing data, and principal component analysis. The ninth chapter covers learning and generalization, including bias-variance trade-off, regularization, and model order selection. The tenth chapter explores Bayesian techniques, including Bayesian learning, model comparison, and Monte Carlo methods. The book also includes appendices on symmetric matrices, Gaussian integrals, Lagrange multipliers, calculus of variations, and principal components, along with references and an index. The text is suitable for students and researchers in computer science, statistics, and machine learning.