This is an excellent book in machine learning and optimization, written by world-experts in the field. The book is informative and lively. The authors use ample illustrations and examples. The concepts are explained thoroughly in simple terms, which makes the book easy to follow. This book introduces concepts that every computer scientist, operational researcher or computational intelligent users should know. It is a valuable reference to both students and practitioner.
Machine learning is one of the most important parts of artificial intelligence. A good book in machine learning is valuable. This book has the quality that matches the best ones published so far. This book covers both supervised learning and unsupervised learning in depth. It also relates them to applications.
Optimization is an important subject. Optimization problems can be found everywhere. No other book could possibly have explained Reactive Search Optimization better than this book, as the authors are inventors and experts in this method. In a nutshell, Reactive Search Optimization is a method that integrates online machine learning techniques into search heuristics.
I find the example applications particularly useful: Text and web mining (for studying web pages and social networks) and collaborative filtering and recommendation (as in internet shopping) are both topical. They are both important big-data applications.
Page created and maintained by Edward Tsang; updated 2014.03.11