Machine Learning An Algorithmic Perspective, Second Edition

Marsland, Stephen

ISBN 10: 1466583282 / ISBN 13: 9781466583283

Edité par Taylor & Francis Group, 2014

A propos de cet article

Ships SAME or NEXT business day. We Ship to APO/FPO addr. Choose EXPEDITED shipping and receive in 2-5 business days within the United States. See our member profile for customer support contact info. We have an easy return policy. N° de réf. du vendeur 53554887

Détails bibliographiques

Titre : Machine Learning An Algorithmic Perspective,...

Éditeur : Taylor & Francis Group

Date d'édition : 2014

Reliure : Couverture rigide

Etat : Like New

Edition : 2ème Édition

A propos de ce titre

Présentation de l'éditeur:

A Proven, Hands-On Approach for Students without a Strong Statistical Foundation

Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area.

Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.

New to the Second Edition

• Two new chapters on deep belief networks and Gaussian processes
• Reorganization of the chapters to make a more natural flow of content
• Revision of the support vector machine material, including a simple implementation for experiments
• New material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptron
• Additional discussions of the Kalman and particle filters
• Improved code, including better use of naming conventions in Python

Suitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the author’s website.