This is a description of the state of the art in machine learning (ML) which includes many exercises and examples designed to give the reader a firm grasp of the concepts and techniques of this expanding subject. Although it develops the necessary theoretical basis of ML, the book begins with an overview suitable for readers lacking a theoretical background. The technical concepts of ML are illustrated using Prolog and the book shows how some of the complex problems inherent in using logic programming as knowledge ...
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This is a description of the state of the art in machine learning (ML) which includes many exercises and examples designed to give the reader a firm grasp of the concepts and techniques of this expanding subject. Although it develops the necessary theoretical basis of ML, the book begins with an overview suitable for readers lacking a theoretical background. The technical concepts of ML are illustrated using Prolog and the book shows how some of the complex problems inherent in using logic programming as knowledge representation may be handled. In addition, it contains the results of a number of leading ML researchers such as Mitchell, Michalski and Winston which are explored and presented for study. The work is intended to be a good starting point from which to learn about ML and should be particularly suitable for undergraduates taking computer science courses but also for those in the first year of an MSc or PhD course.
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