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Machine Learning: An Algorithmic Perspective, Second Edition (Chapman & Hall/Crc Machine Learning & Pattern Recognition)

Amazon.com Price:  $55.35 (as of 05/05/2019 14:12 PST- Details)

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Description

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

Since the best-selling first edition was once 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 continuously find it hard to get started in this area.

Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students be mindful the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics in addition to 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 float of content
  • Revision of the improve 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 side further reading and problems. All the code used to create the examples is to be had on the writer’s website.


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