The preface and table of contents of the book "An Introduction to Support Vector Machines and other kernel-based learning methods" by Nello Cristianini and John Shawe-Taylor provide an overview of the content. The book covers the learning methodology, including supervised learning, generalization, and the advantages and disadvantages of learning methods. It delves into linear learning machines, such as linear classification and regression, and the dual representation of these machines. The book also explores kernel-induced feature spaces, including the implicit mapping into feature space, making kernels, and working in feature space. Generalization theory is discussed, covering PAC learning, VC theory, margin-based bounds, and Bayesian analysis. Optimization theory is covered, including problem formulation, Lagrangian theory, and duality. The book then focuses on Support Vector Machines (SVMs), explaining classification and regression SVMs, and discussing implementation techniques. Finally, it presents applications of SVMs in various fields, such as text categorization, image recognition, bioinformatics, and more. The book includes exercises, further reading, and advanced topics, as well as a pseudocode for the Sequential Minimal Optimization (SMO) algorithm and background mathematics.The preface and table of contents of the book "An Introduction to Support Vector Machines and other kernel-based learning methods" by Nello Cristianini and John Shawe-Taylor provide an overview of the content. The book covers the learning methodology, including supervised learning, generalization, and the advantages and disadvantages of learning methods. It delves into linear learning machines, such as linear classification and regression, and the dual representation of these machines. The book also explores kernel-induced feature spaces, including the implicit mapping into feature space, making kernels, and working in feature space. Generalization theory is discussed, covering PAC learning, VC theory, margin-based bounds, and Bayesian analysis. Optimization theory is covered, including problem formulation, Lagrangian theory, and duality. The book then focuses on Support Vector Machines (SVMs), explaining classification and regression SVMs, and discussing implementation techniques. Finally, it presents applications of SVMs in various fields, such as text categorization, image recognition, bioinformatics, and more. The book includes exercises, further reading, and advanced topics, as well as a pseudocode for the Sequential Minimal Optimization (SMO) algorithm and background mathematics.