Comprehensive treatment of neural networks and machine learning principles, covering supervised and unsupervised learning, multilayer perceptrons, radial basis function networks, support vector machines, kernel methods, recurrent neural networks, self-organizing maps, adaptive filtering, and learning theory. Explains the mathematical foundations, algorithms, architectures, and practical applications of learning machines used in pattern recognition, signal processing, data analysis, artificial intelligence, and computational intelligence systems.