Machine Learning An Algorithmic Perspective Second Edition by Stephen Marsland ISBN 1466583282 978-1466583283

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Product details: 

ISBN 10: 1466583282  

ISBN 13: 978-1466583283

Author: Stephen Marsland

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.

Table of contents: 

Introduction.

Linear Discriminants.

The Multi-Layer Perceptron.

Radial Basis Functions and Splines.

Support Vector Machines.

Learning with Trees.

Decision by Committee: Ensemble Learning.

Probability and Learning. Unsupervised Learning.

Dimensionality Reduction. Optimization and Search.

Evolutionary Learning.

Reinforcement Learning. 

Markov Chain Monte Carlo (MCMC) Methods. Graphical Models.

Python.

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