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paperback. Condition: Very Good. Unmarked trade paperback. Gentle bend on corner of cover.
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Data Science and Machine Learning: Mathematical and Statistical Methods (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
Book 15 of 15: Chapman & Hall/Crc Machine Learning & Pattern RecognitionLanguage: English
Published by Chapman and Hall/CRC, 2019
ISBN 10: 1138492531 ISBN 13: 9781138492530
Hardcover. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
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Data Science and Machine Learning: Mathematical and Statistical Methods (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
Book 15 of 15: Chapman & Hall/Crc Machine Learning & Pattern RecognitionLanguage: English
Published by Chapman and Hall/CRC, 2019
ISBN 10: 1138492531 ISBN 13: 9781138492530
Condition: good. Has a sturdy binding with some shelf wear. May have some markings or highlighting. Used copies may not include access codes or Cd's. Slight bending may be present.
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Condition: As New. Unread book in perfect condition.
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Simulation and the Monte Carlo Method
Book 209 of 358: Wiley Series in Probability and StatisticsSeller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Data Science and Machine Learning: Mathematical and Statistical Methods (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
Book 15 of 15: Chapman & Hall/Crc Machine Learning & Pattern RecognitionLanguage: English
Published by Chapman and Hall/CRC, 2019
ISBN 10: 1138492531 ISBN 13: 9781138492530
Seller: World of Books (was SecondSale), Montgomery, IL, U.S.A.
Seller rating 5 out of 5 stars
Condition: Very Good. Item in very good condition! Textbooks may not include supplemental items i.e. CDs, access codes etc.
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Simulation and the Monte Carlo Method
Book 209 of 358: Wiley Series in Probability and StatisticsSeller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Condition: New. pp. 188 2nd Edition.
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Data Science and Machine Learning : Mathematical and Statistical Methods
Language: English
Published by Chapman and Hall/CRC, 2025
ISBN 10: 1032488689 ISBN 13: 9781032488684
Condition: New.
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Data Science and Machine Learning: Mathematical and Statistical Methods (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
Language: English
Published by Chapman and Hall/CRC, 2025
ISBN 10: 1032488689 ISBN 13: 9781032488684
Condition: New.
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Data Science and Machine Learning: Mathematical and Statistical Methods (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
Language: English
Published by Chapman and Hall/CRC, 2025
ISBN 10: 1032488689 ISBN 13: 9781032488684
Condition: New.
-
Data Science and Machine Learning : Mathematical and Statistical Methods
Language: English
Published by Chapman and Hall/CRC, 2025
ISBN 10: 1032488689 ISBN 13: 9781032488684
Condition: As New. Unread book in perfect condition.
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Data Science and Machine Learning : Mathematical and Statistical Methods
Language: English
Published by Chapman and Hall/CRC, 2025
ISBN 10: 1032488689 ISBN 13: 9781032488684
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Data Science and Machine Learning: Mathematical and Statistical Methods (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
Language: English
Published by Chapman and Hall/CRC, 2025
ISBN 10: 1032488689 ISBN 13: 9781032488684
Condition: New.
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Data Science and Machine Learning: Mathematical and Statistical Methods (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
Language: English
Published by Chapman and Hall/CRC, 2025
ISBN 10: 1032488689 ISBN 13: 9781032488684
hardcover. Condition: New.
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Data Science and Machine Learning : Mathematical and Statistical Methods
Language: English
Published by Chapman and Hall/CRC, 2025
ISBN 10: 1032488689 ISBN 13: 9781032488684
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Condition: acceptable. Used - Acceptable: All pages and the cover are intact, but shrink wrap, dust covers, or boxed set case may be missing. Pages may include limited notes, highlighting, or minor water damage but the text is readable. Pages may include limited notes and highlighting, but the text cannot be obscured or unreadable. Any access codes or passwords originally included with the book may be expired, used or no longer valid. Image is stock photo and cover art edition may be different than pictured.
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Data Science and Machine Learning
Language: English
Published by Taylor and Francis Ltd, GB, 2025
ISBN 10: 1032488689 ISBN 13: 9781032488684
Hardback. Condition: New. 2nd. Praise for the first edition:"In nine succinct but information-packed chapters, the authors provide a logically structured and robust introduction to the mathematical and statistical methods underpinning the still-evolving field of AI and data science."- Joacim Rocklöv and Albert A. Gayle, International Journal of Epidemiology, Volume 49, Issue 6"This book organizes the algorithms clearly and cleverly. The way the Python code was written follows the algorithm closely-very useful for readers who wish to understand the rationale and flow of the background knowledge."- Yin-Ju Lai and Chuhsing Kate Hsiao, Biometrics, Volume 77, Issue 4The purpose of Data Science and Machine Learning: Mathematical and Statistical Methods is to provide an accessible, yet comprehensive textbook intended for students interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science.New in the Second EditionThis expanded edition provides updates across key areas of statistical learning: Monte Carlo Methods: A new section introducing regenerative rejection sampling - a simpler alternative to MCMC. Unsupervised Learning: Inclusion of two multidimensional diffusion kernel density estimators, as well as the bandwidth perturbation matching method for the optimal data-driven bandwidth selection. Regression: New automatic bandwidth selection for local linear regression. Feature Selection and Shrinkage: A new chapter introducing the klimax method for model selection in high-dimensions. Reinforcement Learning: A new chapter on contemporary topics such as policy iteration, temporal difference learning, and policy gradient methods, all complete with Python code. Appendices: Expanded treatment of linear algebra, functional analysis, and optimization that includes the coordinate-descent method and the novel Majorization-Minimization method for constrained optimization.Key Features:Focuses on mathematical understanding.Presentation is self-contained, accessible, and comprehensive.Extensive list of exercises and worked-out examples.Many concrete algorithms with Python code.Full color throughout and extensive indexing.A single-counter consecutive numbering of all theorems, definitions, equations, etc., for easier text searches.
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The Elements of Hawkes Processes
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 96.15
£ 11.98 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In.
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The Elements of Hawkes Processes
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 96.15
£ 11.98 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In.
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Data Science and Machine Learning
Language: English
Published by Taylor and Francis Ltd, GB, 2025
ISBN 10: 1032488689 ISBN 13: 9781032488684
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
Hardback. Condition: New. 2nd. Praise for the first edition:"In nine succinct but information-packed chapters, the authors provide a logically structured and robust introduction to the mathematical and statistical methods underpinning the still-evolving field of AI and data science."- Joacim Rocklöv and Albert A. Gayle, International Journal of Epidemiology, Volume 49, Issue 6"This book organizes the algorithms clearly and cleverly. The way the Python code was written follows the algorithm closely-very useful for readers who wish to understand the rationale and flow of the background knowledge."- Yin-Ju Lai and Chuhsing Kate Hsiao, Biometrics, Volume 77, Issue 4The purpose of Data Science and Machine Learning: Mathematical and Statistical Methods is to provide an accessible, yet comprehensive textbook intended for students interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science.New in the Second EditionThis expanded edition provides updates across key areas of statistical learning: Monte Carlo Methods: A new section introducing regenerative rejection sampling - a simpler alternative to MCMC. Unsupervised Learning: Inclusion of two multidimensional diffusion kernel density estimators, as well as the bandwidth perturbation matching method for the optimal data-driven bandwidth selection. Regression: New automatic bandwidth selection for local linear regression. Feature Selection and Shrinkage: A new chapter introducing the klimax method for model selection in high-dimensions. Reinforcement Learning: A new chapter on contemporary topics such as policy iteration, temporal difference learning, and policy gradient methods, all complete with Python code. Appendices: Expanded treatment of linear algebra, functional analysis, and optimization that includes the coordinate-descent method and the novel Majorization-Minimization method for constrained optimization.Key Features:Focuses on mathematical understanding.Presentation is self-contained, accessible, and comprehensive.Extensive list of exercises and worked-out examples.Many concrete algorithms with Python code.Full color throughout and extensive indexing.A single-counter consecutive numbering of all theorems, definitions, equations, etc., for easier text searches.
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Data Science and Machine Learning : Mathematical and Statistical Methods, 1st Edition
Book 15 of 15: Chapman & Hall/Crc Machine Learning & Pattern RecognitionLanguage: English
Published by Taylor & Francis Group, 2019
ISBN 10: 1138492531 ISBN 13: 9781138492530
Condition: New.
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Condition: New. 1st ed. 2021 edition NO-PA16APR2015-KAP.
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Condition: New. Zdravko I. Botev, PhD, is the pioneer of several modern statistical methodologies, including the diffusion kernel density estimator, the generalized splitting method for rare-event simulation, the bandwidth perturbation matching.
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Condition: New.
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Handbook of Monte Carlo Methods
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
£ 126.37
£ 15 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New.
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Data Science and Machine Learning
Language: English
Published by Taylor and Francis Ltd, GB, 2025
ISBN 10: 1032488689 ISBN 13: 9781032488684
Hardback. Condition: New. 2nd. Praise for the first edition:"In nine succinct but information-packed chapters, the authors provide a logically structured and robust introduction to the mathematical and statistical methods underpinning the still-evolving field of AI and data science."- Joacim Rocklöv and Albert A. Gayle, International Journal of Epidemiology, Volume 49, Issue 6"This book organizes the algorithms clearly and cleverly. The way the Python code was written follows the algorithm closely-very useful for readers who wish to understand the rationale and flow of the background knowledge."- Yin-Ju Lai and Chuhsing Kate Hsiao, Biometrics, Volume 77, Issue 4The purpose of Data Science and Machine Learning: Mathematical and Statistical Methods is to provide an accessible, yet comprehensive textbook intended for students interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science.New in the Second EditionThis expanded edition provides updates across key areas of statistical learning: Monte Carlo Methods: A new section introducing regenerative rejection sampling - a simpler alternative to MCMC. Unsupervised Learning: Inclusion of two multidimensional diffusion kernel density estimators, as well as the bandwidth perturbation matching method for the optimal data-driven bandwidth selection. Regression: New automatic bandwidth selection for local linear regression. Feature Selection and Shrinkage: A new chapter introducing the klimax method for model selection in high-dimensions. Reinforcement Learning: A new chapter on contemporary topics such as policy iteration, temporal difference learning, and policy gradient methods, all complete with Python code. Appendices: Expanded treatment of linear algebra, functional analysis, and optimization that includes the coordinate-descent method and the novel Majorization-Minimization method for constrained optimization.Key Features:Focuses on mathematical understanding.Presentation is self-contained, accessible, and comprehensive.Extensive list of exercises and worked-out examples.Many concrete algorithms with Python code.Full color throughout and extensive indexing.A single-counter consecutive numbering of all theorems, definitions, equations, etc., for easier text searches.
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Paperback. Condition: Brand New. 147 pages. 9.25x6.10x0.32 inches. In Stock.
