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Machine learning : an algorithmic perspective / Stephen Marsland.

By: Material type: TextTextSeries: Chapman & Hall/CRC machine learning & pattern recognition seriesPublisher: Boca Raton : CRC Press, [2015]Edition: 2nd. edDescription: xx, 437 pages : illustrations ; 25 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9781466583283 (hbk)
  • 1466583282 (hbk)
Subject(s): DDC classification:
  • 22 006.31 M.S.M
LOC classification:
  • Q325.5 .M368 2015
Online resources:
Contents:
Introduction -- Preliminaries -- Neurons, neural networks, and linear discriminants -- The multi-layer perceptron -- Radial basis functions and splines -- Dimensionality reduction -- Probabilistic learning -- Support vector machines -- Optimisation and search -- Evolutionary learning -- Reinforcement learning -- Learning with trees -- Decision by committee: ensemble learning -- Unsupervised learning -- Markov chain Monte Carlo (MCMC) methods -- Graphical models -- Symmetric weights and deep belief networks -- Gaussian processes -- Python.
Summary: Annotation Written in an easily accessible style, this text provides the ideal blend of theory and practical, applicable knowledge. It covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization.
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Holdings
Item type Current library Call number Status Date due Barcode
Books Books Main library A2 006.31 M.S.M (Browse shelf(Opens below)) Available 00012566

"A Chapman & Hall book."

Includes bibliographical references and index.

Introduction -- Preliminaries -- Neurons, neural networks, and linear discriminants -- The multi-layer perceptron -- Radial basis functions and splines -- Dimensionality reduction -- Probabilistic learning -- Support vector machines -- Optimisation and search -- Evolutionary learning -- Reinforcement learning -- Learning with trees -- Decision by committee: ensemble learning -- Unsupervised learning -- Markov chain Monte Carlo (MCMC) methods -- Graphical models -- Symmetric weights and deep belief networks -- Gaussian processes -- Python.

Annotation
Written in an easily accessible style, this text provides the ideal blend of theory and practical, applicable knowledge. It covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization.

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