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ISBN 10: 0367574640 ISBN 13: 9780367574642
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Published by Chapman and Hall/CRC (edition 2), 2020
ISBN 10: 0367574640 ISBN 13: 9780367574642
Language: English
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Published by Chapman and Hall/CRC, 2020
ISBN 10: 0367574640 ISBN 13: 9780367574642
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Published by Chapman and Hall/CRC, 2020
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Paperback or Softback. Condition: New. A First Course in Machine Learning 1.31. Book.
Published by Chapman and Hall/CRC, 2020
ISBN 10: 0367574640 ISBN 13: 9780367574642
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Published by Chapman and Hall/CRC, 2016
ISBN 10: 1498738486 ISBN 13: 9781498738484
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Published by Taylor & Francis Group, 2020
ISBN 10: 0367574640 ISBN 13: 9780367574642
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Published by Chapman and Hall/CRC, 2020
ISBN 10: 0367574640 ISBN 13: 9780367574642
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ISBN 10: 1032937904 ISBN 13: 9781032937908
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ISBN 10: 1032937904 ISBN 13: 9781032937908
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Published by Taylor & Francis Ltd, 2020
ISBN 10: 0367574640 ISBN 13: 9780367574642
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Published by Taylor & Francis Group, 2020
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Published by Chapman and Hall/CRC 2020-06, 2020
ISBN 10: 0367574640 ISBN 13: 9780367574642
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Published by Chapman and Hall/CRC, 2020
ISBN 10: 0367574640 ISBN 13: 9780367574642
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Published by Taylor & Francis Ltd, 2020
ISBN 10: 0367574640 ISBN 13: 9780367574642
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Published by T&F INDIA, 2025
ISBN 10: 1032937904 ISBN 13: 9781032937908
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ISBN 10: 1032937904 ISBN 13: 9781032937908
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Published by Taylor & Francis Group, 2020
ISBN 10: 0367574640 ISBN 13: 9780367574642
Language: English
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Published by Chapman and Hall/CRC, 2016
ISBN 10: 1498738486 ISBN 13: 9781498738484
Language: English
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Published by Taylor & Francis Ltd, 2020
ISBN 10: 0367574640 ISBN 13: 9780367574642
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Published by Chapman and Hall/CRC, 2016
ISBN 10: 1498738486 ISBN 13: 9781498738484
Language: English
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Published by Chapman and Hall/CRC, 2016
ISBN 10: 1498738486 ISBN 13: 9781498738484
Language: English
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Published by Taylor & Francis Inc, Portland, 2016
ISBN 10: 1498738486 ISBN 13: 9781498738484
Language: English
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Hardcover. Condition: new. Hardcover. "A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC."Devdatt Dubhashi, Professor, Department of Computer Science and Engineering, Chalmers University, Sweden"This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade."Daniel Barbara, George Mason University, Fairfax, Virginia, USA"The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling, inference, and prediction, providing just in time the essential background on linear algebra, calculus, and probability theory that the reader needs to understand these concepts."Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg, Denmark"I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strengthOverall, this is a pragmatic and helpful book, which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months."David Clifton, University of Oxford, UK"The first edition of this book was already an excellent introductory text on machine learning for an advanced undergraduate or taught masters level course, or indeed for anybody who wants to learn about an interesting and important field of computer science. The additional chapters of advanced material on Gaussian process, MCMC and mixture modeling provide an ideal basis for practical projects, without disturbing the very clear and readable exposition of the basics contained in the first part of the book." Gavin Cawley, Senior Lecturer, School of Computing Sciences, University of East Anglia, UK"This book could be used for junior/senior undergraduate students or first-year graduate students, as well as individuals who want to explore the field of machine learningThe book introduces not only the concepts but the underlying ideas on algorithm implementation from a critical thinking perspective."Guangzhi Qu, Oakland University, Rochester, Michigan, USA 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.