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Preview — Machine Learning by Stephen Marsland. 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 backgro 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.

2nd Edition

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. Get A Copy. Hardcover , pages.

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More Details Other Editions 1. Friend Reviews. To see what your friends thought of this book, please sign up. To ask other readers questions about Machine Learning , please sign up. I am new to machine learning can any one suggest a few good reading. See 1 question about Machine Learning…. Lists with This Book. Perfect Condition. More information about this seller Contact this seller 9. From: Romtrade Corp. This item may ship fro the US or other locations in India depending on your location and availability. More information about this seller Contact this seller Excellent Customer Service.

This is Brand New. Seller Inventory Adhya Published by Taylor Fransis. About this Item: Taylor Fransis. Brand New. Language: English. Brand new Book. A Proven, Hands-On Approach for Students without a Strong Statistical FoundationSince 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.

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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 EditionTwo new chapters on deep belief networks and Gaussian processes Reorganization of the chapters to make a more natural flow of contentRevision of the support vector machine material, including a simple implementation for experimentsNew material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptronAdditional discussions of the Kalman and particle filtersImproved code, including better use of naming conventions in PythonSuitable 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. Seller Inventory AA Sonstige Video.

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Condition: Neu. Neuware - pp. Light rubbing wear to cover, spine and page edges.


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Very minimal writing or notations in margins not affecting the text. Possible clean ex-library copy, with their stickers and or stamp s. Condition: Used: Very Good. Seller Inventory D1S Great condition with minimal wear, aging, or shelf wear. Seller Inventory P Condition: Like New. Almost new condition. Condition: Good. Ships with Tracking Number! May not contain Access Codes or Supplements. May be ex-library. Buy with confidence, excellent customer service!. Seller Inventory q. Condition: GOOD.

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Has little wear to the cover and pages. Contains some markings such as highlighting and writing.

Machine Learning: An Algorithmic Perspective by Stephen Marsland

Supplemental materials are not guaranteed with any used book purchases. Pages are intact and are not marred by notes or highlighting, but may contain a neat previous owner name. The spine remains undamaged. Seller Inventory NEW Never used!. Seller Inventory n. Item added to your basket View basket.