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Neural Networks And Learning Machines is a complete introduction to the concepts and theories of neural networks. It has been written primarily for engineering students. Summary Of The Book Neural Networks And Learning Machines is a text on the subject of neural networks and learning systems. It has been updated to include the latest advancements in the field. Neural networks are groups of connected nodes or artificial neurons. They are based on the biological neural network and work together in problem solving. These types of machines are mostly built to be adaptive i.e. they are capable of learning and changing their behavior based on some training processes. Such networks are used in predictive modeling of behavior and in robotics. There are different types of neural networks, which use different kinds of learning algorithms for various tasks. The book starts with a concise introduction to the concept of neural networks. It discusses concepts like the human brain, the neural network, models of neurons, and network architecture. It also covers knowledge representation as well as learning processes and tasks. Some of the chapters in the book are Rosenblatt’s Perceptron, Model Building Through Regression, Multilayer Perceptrons, Least Mean Square Algorithm, Kernel Methods, Radial-Basis Function Networks, and Support Vector Machines. Chapters like Principal-Components Analysis, Regularization Theory, Self Organizing Maps, and Scholastic Methods Rooted in Statistical Mechanism are also included. Neural Networks And Learning Machines presents other chapters such as Neurodynamics, Bayesian Filtering for State Estimation of Dynamic Systems, Dynamic Programming, and Dynamically Driven Recurrent Networks. Topics like kernel methods, information-theoretic models, and recurrent neural networks have also been presented. Readers are familiarized with helpful computer-oriented experiments and are provided online help in learning algorithms. This edition of Neural Networks And Learning Machines has extended coverage of semi-supervised and supervised learning for solving large-scale problems. About Simon Haykin Simon Haykin is an electrical engineer and author. He has written academic books on communication systems and neural networks. Some of his books are Statistical Communication Theory, Introduction To Analog And Digital Communications, Adaptive Filter Theory, and Neural Networks: A Comprehensive Foundation. Haykin studied at the University of Birmingham, England, where he earned a B.Sc, D.Sc, and a PhD in Electrical Engineering. He is an expert on adaptive signal processing. Haykin has received several honors including an honorary degree from ETH Zentrum, Switzerland, and the Henry Booker Gold Medal.