Machine Learning: An Algorithmic Perspective (Chapman & Hall/Crc Machine Learning & Patrtern Recognition)

Marsland, Stephen Stephen Marsland,

ISBN 10: 1420067184 / ISBN 13: 9781420067187

Verlag: Chapman and Hall/CRC, 2009

Beschreibung

Machine Learning: An Algorithmic Perspective (Chapman & Hall/Crc Machine Learning & Patrtern Recognition) This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping. .

Bestandsnummer des Verkäufers 7719-9781420067187

Bibliografische Details

• Titel: Machine Learning: An Algorithmic Perspective (Chapman & Hall/Crc Machine Learning & Patrtern Recognition)
• Verlag: Chapman and Hall/CRC
• Veröffentlichungsdatum: 2009
• Sprache: Englisch
• ISBN-10: 1420067184
• ISBN-13: 9781420067187
• Bindung: paperback
• Zustand: Very Good

Über diesen Titel

Inhaltsangabe

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.

Über die Autorin bzw. den Autor

Massey University, Palmerston North, New Zealand