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Linear Algebra, Data Science, and Machine Learning (Springer Undergraduate Texts in Mathematics and Technology)
Book 32 of 32: Springer Undergraduate Texts in Mathematics and TechnologySeller: Books From California, Simi Valley, CA, U.S.A.
Seller rating 4 out of 5 stars
hardcover. Condition: Fine.
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Linear Algebra, Data Science, and Machine Learning (Springer Undergraduate Texts in Mathematics and Technology)
Book 32 of 32: Springer Undergraduate Texts in Mathematics and TechnologySeller: Books From California, Simi Valley, CA, U.S.A.
Seller rating 4 out of 5 stars
hardcover. Condition: Good.
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Linear Algebra, Data Science, and Machine Learning (Springer Undergraduate Texts in Mathematics and Technology)
Book 32 of 32: Springer Undergraduate Texts in Mathematics and TechnologySeller: Books From California, Simi Valley, CA, U.S.A.
Seller rating 4 out of 5 stars
hardcover. Condition: Very Good.
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Condition: As New. Unread book in perfect condition.
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Linear Algebra, Data Science, and Machine Learning
Book 32 of 32: Springer Undergraduate Texts in Mathematics and TechnologySeller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 5 out of 5 stars
HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.
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Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
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Condition: New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.
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Condition: New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.
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Linear Algebra, Data Science and Machine Learning
Book 32 of 32: Springer Undergraduate Texts in Mathematics and TechnologySeller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Linear Algebra, Data Science, and Machine Learning
Book 32 of 32: Springer Undergraduate Texts in Mathematics and TechnologyLanguage: English
Published by Springer International Publishing AG, CH, 2025
ISBN 10: 3031937635 ISBN 13: 9783031937637
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
Hardback. Condition: New.
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Linear Algebra, Data Science and Machine Learning
Book 32 of 32: Springer Undergraduate Texts in Mathematics and TechnologySeller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Linear Algebra Data Science & Machine Le
Book 32 of 32: Springer Undergraduate Texts in Mathematics and TechnologySeller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
Seller rating 5 out of 5 stars
Condition: New.
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hardcover. Condition: VeryGood. A copy that may have been read, very minimal wear and tear. May have a remainder mark.
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Linear Algebra, Data Science, and Machine Learning
Book 32 of 32: Springer Undergraduate Texts in Mathematics and TechnologyLanguage: English
Published by Springer International Publishing AG, CH, 2025
ISBN 10: 3031937635 ISBN 13: 9783031937637
Hardback. Condition: New.
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Linear Algebra, Data Science, and Machine Learning
Book 32 of 32: Springer Undergraduate Texts in Mathematics and TechnologyLanguage: English
Published by Springer Verlag GmbH, 2025
ISBN 10: 3031937635 ISBN 13: 9783031937637
Condition: New.
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Hardcover. Condition: Brand New. 652 pages. 10.00x7.00x10.00 inches. In Stock.
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This text provides a mathematically rigorous introduction to modern methods of machine learning and data analysis at the advanced undergraduate/beginning graduate level. The book is self-contained and requires minimal mathematical prerequisites. There is a strong focus on learning how and why algorithms work, as well as developing facility with their practical applications. Apart from basic calculus, the underlying mathematics linear algebra, optimization, elementary probability, graph theory, and statistics is developed from scratch in a form best suited to the overall goals. In particular, the wide-ranging linear algebra components are unique in their ordering and choice of topics, emphasizing those parts of the theory and techniques that are used in contemporary machine learning and data analysis. The book will provide a firm foundation to the reader whose goal is to work on applications of machine learning and/or research into the further development of this highly active field of contemporary applied mathematics.To introduce the reader to a broad range of machine learning algorithms and how they are used in real world applications, the programming language Python is employed and offers a platform for many of the computational exercises. Python not Elektronisches Buch complementing various topics in the book are available on a companion GitHub site specified in the Preface, and can be easily accessed by scanning the QR codes or clicking on the links provided within the text. Exercises appear at the end of each section, including basic ones designed to test comprehension and computational skills, while others range over proofs not supplied in the text, practical computations, additional theoretical results, and further developments in the subject. The Students Solutions Manual may be accessed from GitHub. Instructors may apply for access to the Instructors Solutions Manual from the link supplied on the text s Springer website.The book can be used in a junior or senior level course for students majoring in mathematics with a focus on applications as well as students from other disciplines who desire to learn the tools of modern applied linear algebra and optimization. It may also be used as an introduction to fundamental techniques in data science and machine learning for advanced undergraduate and graduate students or researchers from other areas, including statistics, computer science, engineering, biology, economics and finance, and so on.
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Linear Algebra, Data Science, and Machine Learning
Book 32 of 32: Springer Undergraduate Texts in Mathematics and TechnologyLanguage: English
Published by Springer International Publishing AG, CH, 2025
ISBN 10: 3031937635 ISBN 13: 9783031937637
Hardback. Condition: New.
-
Linear Algebra, Data Science, and Machine Learning
Book 32 of 32: Springer Undergraduate Texts in Mathematics and TechnologyLanguage: English
Published by Springer International Publishing AG, CH, 2025
ISBN 10: 3031937635 ISBN 13: 9783031937637
Hardback. Condition: New.
-
Condition: new. Questo è un articolo print on demand.
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Hardcover. Condition: Brand New. 652 pages. 10.00x7.00x10.00 inches. In Stock. This item is printed on demand.
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Linear Algebra, Data Science, and Machine Learning
Book 32 of 32: Springer Undergraduate Texts in Mathematics and TechnologyLanguage: English
Published by Springer, Springer Aug 2025, 2025
ISBN 10: 3031937635 ISBN 13: 9783031937637
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This text provides a mathematically rigorous introduction to modern methods of machine learning and data analysis at the advanced undergraduate/beginning graduate level. The book is self-contained and requires minimal mathematical prerequisites. There is a strong focus on learning how and why algorithms work, as well as developing facility with their practical applications. Apart from basic calculus, the underlying mathematics linear algebra, optimization, elementary probability, graph theory, and statistics is developed from scratch in a form best suited to the overall goals. In particular, the wide-ranging linear algebra components are unique in their ordering and choice of topics, emphasizing those parts of the theory and techniques that are used in contemporary machine learning and data analysis. The book will provide a firm foundation to the reader whose goal is to work on applications of machine learning and/or research into the further development of this highly active field of contemporary applied mathematics.To introduce the reader to a broad range of machine learning algorithms and how they are used in real world applications, the programming language Python is employed and offers a platform for many of the computational exercises. Python not Elektronisches Buch complementing various topics in the book are available on a companion GitHub site specified in the Preface, and can be easily accessed by scanning the QR codes or clicking on the links provided within the text. Exercises appear at the end of each section, including basic ones designed to test comprehension and computational skills, while others range over proofs not supplied in the text, practical computations, additional theoretical results, and further developments in the subject. The Students Solutions Manual may be accessed from GitHub. Instructors may apply for access to the Instructors Solutions Manual from the link supplied on the text s Springer website.The book can be used in a junior or senior level course for students majoring in mathematics with a focus on applications as well as students from other disciplines who desire to learn the tools of modern applied linear algebra and optimization. It may also be used as an introduction to fundamental techniques in data science and machine learning for advanced undergraduate and graduate students or researchers from other areas, including statistics, computer science, engineering, biology, economics and finance, and so on. 656 pp. Englisch.
