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Mathematical Foundations Data Analysis by Phillips Jeff (35 results)
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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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Mathematical Foundations for Data Analysis (Springer Series in the Data Sciences)
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paperback. Condition: Good.
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Mathematical Foundations for Data Analysis (Springer Series in the Data Sciences)
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Condition: New. pp. 287.
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Mathematical Foundations for Data Analysis
Published by Springer Nature Switzerland AG, 2021
ISBN 10: 3030623408 ISBN 13: 9783030623401
Language: English
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Mathematical Foundations for Data Analysis
Published by Springer Nature Switzerland AG, 2021
ISBN 10: 3030623408 ISBN 13: 9783030623401
Language: English
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Mathematical Foundations for Data Analysis
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Mathematical Foundations for Data Analysis (Springer Series in the Data Sciences)
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Mathematical Foundations for Data Analysis
Published by Springer Nature Switzerland AG, CH, 2021
ISBN 10: 3030623408 ISBN 13: 9783030623401
Language: English
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
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Add to basketHardback. Condition: New. 2021 ed. This textbook, suitable for an early undergraduate up to a graduate course, provides an overview of many basic principles and techniques needed for modern data analysis. In particular, this book was designed and written as preparation for students planning to take rigorous Machine Learning and Data Mining courses. It introduces key conceptual tools necessary for data analysis, including concentration of measure and PAC bounds, cross validation, gradient descent, and principal component analysis. It also surveys basic techniques in supervised (regression and classification) and unsupervised learning (dimensionality reduction and clustering) through an accessible, simplified presentation. Students are recommended to have some background in calculus, probability, and linear algebra. Some familiarity with programming and algorithms is useful to understand advanced topics on computational techniques.
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Mathematical Foundations for Data Analysis
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Mathematical Foundations for Data Analysis (Springer Series in the Data Sciences)
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Add to basketHardcover. Condition: Brand New. 282 pages. 9.25x6.10x0.83 inches. In Stock.
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Add to basketHardcover. Condition: Brand New. 282 pages. 9.25x6.10x0.83 inches. In Stock.
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Mathematical Foundations for Data Analysis
Published by Springer International Publishing, 2021
ISBN 10: 3030623408 ISBN 13: 9783030623401
Language: English
US$ 76.52
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Add to basketCondition: New. Provides accessible, simplified introduction to core mathematical language and conceptsIntegrates examples of key concepts through geometric illustrations and Python codingAddresses topics in locality sensitive .
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Mathematical Foundations for Data Analysis
Published by Springer International Publishing, Springer Nature Switzerland Mär 2022, 2022
ISBN 10: 3030623432 ISBN 13: 9783030623432
Language: English
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
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Add to basketTaschenbuch. Condition: Neu. Neuware -This textbook, suitable for an early undergraduate up to a graduate course, provides an overview of many basic principles and techniques needed for modern data analysis. In particular, this book was designed and written as preparation for students planning to take rigorous Machine Learning and Data Mining courses. It introduces key conceptual tools necessary for data analysis, including concentration of measure and PAC bounds, cross validation, gradient descent, and principal component analysis. It also surveys basic techniques in supervised (regression and classification) and unsupervised learning (dimensionality reduction and clustering) through an accessible, simplified presentation. Students are recommended to have some background in calculus, probability, and linear algebra. Some familiarity with programming and algorithms is useful to understand advanced topics on computational techniques.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 308 pp. Englisch.
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Mathematical Foundations for Data Analysis
Published by Springer International Publishing, Springer International Publishing Mär 2021, 2021
ISBN 10: 3030623408 ISBN 13: 9783030623401
Language: English
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
US$ 70.86
Convert currencyUS$ 70.15 shipping from Germany to U.S.A.Quantity: 2 available
Add to basketBuch. Condition: Neu. Neuware -This textbook, suitable for an early undergraduate up to a graduate course, provides an overview of many basic principles and techniques needed for modern data analysis. In particular, this book was designed and written as preparation for students planning to take rigorous Machine Learning and Data Mining courses. It introduces key conceptual tools necessary for data analysis, including concentration of measure and PAC bounds, cross validation, gradient descent, and principal component analysis. It also surveys basic techniques in supervised (regression and classification) and unsupervised learning (dimensionality reduction and clustering) through an accessible, simplified presentation. Students are recommended to have some background in calculus, probability, and linear algebra. Some familiarity with programming and algorithms is useful to understand advanced topics on computational techniques.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 308 pp. Englisch.
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Mathematical Foundations for Data Analysis
Published by Springer International Publishing, 2022
ISBN 10: 3030623432 ISBN 13: 9783030623432
Language: English
US$ 70.86
Convert currencyUS$ 72.89 shipping from Germany to U.S.A.Quantity: 1 available
Add to basketTaschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This textbook, suitable for an early undergraduate up to a graduate course, provides an overview of many basic principles and techniques needed for modern data analysis. In particular, this book was designed and written as preparation for students planning to take rigorous Machine Learning and Data Mining courses. It introduces key conceptual tools necessary for data analysis, including concentration of measure and PAC bounds, cross validation, gradient descent, and principal component analysis. It also surveys basic techniques in supervised (regression and classification) and unsupervised learning (dimensionality reduction and clustering) through an accessible, simplified presentation. Students are recommended to have some background in calculus, probability, and linear algebra. Some familiarity with programming and algorithms is useful to understand advanced topics on computational techniques.
-
Mathematical Foundations for Data Analysis
Published by Springer International Publishing, Springer International Publishing, 2021
ISBN 10: 3030623408 ISBN 13: 9783030623401
Language: English
US$ 70.86
Convert currencyUS$ 74.16 shipping from Germany to U.S.A.Quantity: 2 available
Add to basketBuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This textbook, suitable for an early undergraduate up to a graduate course, provides an overview of many basic principles and techniques needed for modern data analysis. In particular, this book was designed and written as preparation for students planning to take rigorous Machine Learning and Data Mining courses. It introduces key conceptual tools necessary for data analysis, including concentration of measure and PAC bounds, cross validation, gradient descent, and principal component analysis. It also surveys basic techniques in supervised (regression and classification) and unsupervised learning (dimensionality reduction and clustering) through an accessible, simplified presentation. Students are recommended to have some background in calculus, probability, and linear algebra. Some familiarity with programming and algorithms is useful to understand advanced topics on computational techniques.
-
Mathematical Foundations for Data Analysis
Published by Springer Nature Switzerland AG, CH, 2021
ISBN 10: 3030623408 ISBN 13: 9783030623401
Language: English
US$ 85.34
Convert currencyUS$ 87.77 shipping from United Kingdom to U.S.A.Quantity: 1 available
Add to basketHardback. Condition: New. 2021 ed. This textbook, suitable for an early undergraduate up to a graduate course, provides an overview of many basic principles and techniques needed for modern data analysis. In particular, this book was designed and written as preparation for students planning to take rigorous Machine Learning and Data Mining courses. It introduces key conceptual tools necessary for data analysis, including concentration of measure and PAC bounds, cross validation, gradient descent, and principal component analysis. It also surveys basic techniques in supervised (regression and classification) and unsupervised learning (dimensionality reduction and clustering) through an accessible, simplified presentation. Students are recommended to have some background in calculus, probability, and linear algebra. Some familiarity with programming and algorithms is useful to understand advanced topics on computational techniques.
