Neural Networks and Learning Machines (3rd Edition)

For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science.

Neural Networks and Learning Machines, Third Edition is renowned for its thoroughness and readability. This well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. This is ideal for professional engineers and research scientists.

Matlab codes used for the computer experiments in the text are available for download at: http://www.pearsonhighered.com/haykin/

Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together. Ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks beyond the capability of either independently.

• Pattern Recognition and Machine Learning (Information Science and Statistics)
• Deep Learning (Adaptive Computation and Machine Learning series)
• Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series)
• The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
• Convex Optimization
• Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory (v. 1)
• Make Your Own Neural Network
• Deep Learning with Python
• Fundamentals of Statistical Signal Processing, Volume II: Detection Theory
• Causality: Models, Reasoning and Inference