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

By: Rogers, Simon, authorContributor(s): Girolami, Mark, authorMaterial type: TextTextSeries: Chapman & Hall/CRC machine learning & pattern recognition seriesPublisher: Boca Raton : CRC Press, c2017Edition: Second editionDescription: xxix, 397 pages : illustrations (black and white) ; 24 cmISBN: 978-14-8738484 (Hardback)Subject(s): Machine learning | Computers and ITLOC 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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Item type Current location Collection Shelving location Call number Copy number Status Date due Barcode
Book Book Cavite State University - CCAT Campus
Book GCS CIR Q 325.5 R64 2017 (Browse shelf) 1 Available R0011731
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CIR Q 158.5 M33 2014 Physical science / CIR Q 183.3.A1 K44 2005 Science curriculum topic study : bridging the gap between standards and practice / CIR Q 325.5 M57 2020 Machine learning for iOS developers / CIR Q 325.5 R64 2017 A first course in machine learning / CIR QA 11.2 S88 2007 Teaching for learning mathematics / CIR QA 11.2 T43 2008 Teaching secondary school mathematics : a resource book / CIR QA 37.3 B55c 2007 v.1 College Mathematics I /

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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