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A first course in machine learning / Simon Rogers; Mark Girolami.

By: Contributor(s): Material type: TextSeries: Chapman & Hall/CRC machine learning & pattern recognition seriesPublication details: Boca Raton : CRC Press, c2017.Edition: Second editionDescription: xxix, 397 pages : illustrations (black and white) ; 24 cmISBN:
  • 978-14-8738484 (Hardback)
Subject(s): LOC classification:
  • CIR Q 325.5  R64 2017
Contents:
Linear Modelling: A Least Squares Approach. Linear Modelling: A Maximum Likelihood Approach. The Bayesian Approach to Machine Learning. Bayesian Inference. Classification. Clustering. Principal Components Analysis and Latent Variable Models. Further Topics in Markov Chain Monte Carlo. Classification and Regression with Gaussian Processes. Dirichlet Process models.
Summary: The new edition of this popular, undergraduate textbook has been revised and updated to reflect current growth areas in Machine Learning. The new edition includes three new chapters with more detailed discussion of Markov Chain Monte Carlo techniques, Classification and Regression with Gaussian Processes, and Dirichlet Process models.
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Cover image Item type Current library Home library Collection Shelving location Call number Materials specified Vol info URL Copy number Status Notes Date due Barcode Item holds Item hold queue priority Course reserves
Book Cavite State University - CCAT Campus Book GCS CIR Q 325.5 R64 2017 (Browse shelf(Opens below)) 1 Available R0011731

Includes e-book access.
Previous edition: 2012.

"A Chapman & Hall book."

Includes bibliographical references and index.

Linear Modelling: A Least Squares Approach. Linear Modelling: A Maximum Likelihood Approach. The Bayesian Approach to Machine Learning. Bayesian Inference. Classification. Clustering. Principal Components Analysis and Latent Variable Models. Further Topics in Markov Chain Monte Carlo. Classification and Regression with Gaussian Processes. Dirichlet Process models.

The new edition of this popular, undergraduate textbook has been revised and updated to reflect current growth areas in Machine Learning. The new edition includes three new chapters with more detailed discussion of Markov Chain Monte Carlo techniques, Classification and Regression with Gaussian Processes, and Dirichlet Process models.

In English text.

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