Machine Learning: An Algorithmic Perspective

Stephen Marsland

ISBN 10: 1420067184 / ISBN 13: 9781420067187

Edité par CRC Press, 2009

A propos de cet article

Very light wear to boards. Pages crisp and clean. Appears free of writing / highlighting. N° de réf. du vendeur 040845

Détails bibliographiques

Titre : Machine Learning: An Algorithmic Perspective

Éditeur : CRC Press

Date d'édition : 2009

Reliure : Hardcover

Etat : Very Good

Edition : 1st Edition

A propos de ce titre

Synopsis :

Unusual book

Présentation de l'éditeur:

Traditional books on machine learning can be divided into two groups — those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text.

Theory Backed up by Practical Examples

The book covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equations and mathematical concepts. The author addresses the topics in a practical way while providing complete information and references where other expositions can be found. He includes examples based on widely available datasets and practical and theoretical problems to test understanding and application of the material. The book describes algorithms with code examples backed up by a website that provides working implementations in Python. The author uses data from a variety of applications to demonstrate the methods and includes practical problems for students to solve.

Highlights a Range of Disciplines and Applications

Drawing from computer science, statistics, mathematics, and engineering, the multidisciplinary nature of machine learning is underscored by its applicability to areas ranging from finance to biology and medicine to physics and chemistry. Written in an easily accessible style, this book bridges the gaps between disciplines, providing the ideal blend of theory and practical, applicable knowledge.