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“This book covers many areas related to my module. I would be happy to recommend this book to my students. I believe my students would be able to follow this book without any difficulty. Book chapters are very well organised and this will help me to pick and choose the subjects related to this module.” Dr Ahmad Lotfi, Nottingham Trent University, UK

Chapter 1. Introduction Chapter 2. Probability and Stochastic Processes Chapter 3. Learning in Parametric Modeling: Basic Concepts and Directions Chapter 4: Mean-Square Error Linear Estimation Chapter 5. Stochastic Gradient Descent: The LMS Algorithm and Its Family Chapter 6. The Least-Squares Family Chapter 7. Classification: A Tour of the Classics Chapter 8. Parameter Learning: A Convex Analytic Path Chapter 9. Sparsity-Aware Learning: Concepts and Theoretical Foundations Chapter 10. Sparsity-Aware Learning: Algorithms and Applications Chapter 11. Learning in Reproducing Kernel Hilbert Spaces Chapter 12. Bayesian Learning: Inference and the EM Algorithm Chapter 13. Bayesian Learning: Approximate Inference and Nonparametric Models Chapter 14. Monte Carlo Methods Chapter 15. Probabilistic Graphical Models: Part 1 Chapter 16. Probabilistic Graphical Models: Part 2 Chapter 17. Particle Filtering Chapter 18. Neural Networks and Deep Learning Chapter 19. Dimensionality Reduction and Latent Variables Modeling

Negnevitsky shows students how to build intelligent systems drawing on techniques from knowledge-based systems, neural networks, fuzzy systems, evolutionary computation and now also intelligent agents. The principles behind these techniques are explained without resorting to complex mathematics, showing how the various techniques are implemented, when they are useful and when they are not. No particular programming language is assumed and the book does not tie itself to any of the software tools available. However, available tools and their uses are described, and program examples are given in Java.

The lack of assumed prior knowledge makes this book ideal for any introductory courses in artificial intelligence or intelligent systems design, while the contemporary coverage means more advanced students will benefit by discovering the latest state-of-the-art techniques, particularly in intelligent agents and knowledge discovery.