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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: Second editionDescription: xx, 437 pages : illustrations ; 25 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9781466583283 (hbk)
  • 1466583282 (hbk)
Subject(s): DDC classification:
  • 006.31 MAR/M
LOC classification:
  • Q325.5 .M368 2015
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.
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Holdings
Item type Current library Collection Call number Status Notes Date due Barcode
Books Books MES KC LIBRARY COMPUTER SCIENCE Data Science & Analytics 006.31 MAR/M (Browse shelf(Opens below)) Available GL33R2 41835
Browsing MES KC LIBRARY shelves, Shelving location: COMPUTER SCIENCE, Collection: Data Science & Analytics Close shelf browser (Hides shelf browser)
006.3 SCH/D Doing data science / 006.302855133 BRA/P Prolog programming for artificial intelligence / 006.31 HAS/E The elements of statistical learning : 006.31 MAR/M Machine learning : 006.31 MIT/M Machine Learning / 006.312 ERI/B Big data fundamentals : 006.312 ERI/B Big data fundamentals :

"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.

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