Calder Jeff (30 results)
Language: English
Published by Springer, 2025
Series: Book 32 of 32 - Springer Undergraduate Texts in Mathematics and Technology
- Hardcover
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hardcover. Condition: Good.
Language: English
Published by Springer, 2025
Series: Book 32 of 32 - Springer Undergraduate Texts in Mathematics and Technology
- Hardcover
Seller: Books From California, Simi Valley, CA, U.S.A.Books From California
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hardcover. Condition: Very Good.
Language: English
Published by Springer, 2025
Series: Book 32 of 32 - Springer Undergraduate Texts in Mathematics and Technology
- Hardcover
Seller: Romtrade Corp., STERLING HEIGHTS, MI, U.S.A.Romtrade Corp.
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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.
Language: English
Published by Springer, 2025
Series: Book 32 of 32 - Springer Undergraduate Texts in Mathematics and Technology
- Hardcover
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Language: English
Published by Springer, 2025
Series: Book 32 of 32 - Springer Undergraduate Texts in Mathematics and Technology
- Hardcover
Seller: SMASS Sellers, IRVING, TX, U.S.A.SMASS Sellers
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Language: English
Published by Springer, 2025
Series: Book 32 of 32 - Springer Undergraduate Texts in Mathematics and Technology
- Hardcover
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
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Language: English
Published by Springer, 2025
Series: Book 32 of 32 - Springer Undergraduate Texts in Mathematics and Technology
- Hardcover
Seller: Majestic Books, Hounslow, United KingdomMajestic Books
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Language: English
Published by Springer, 2025
Series: Book 32 of 32 - Springer Undergraduate Texts in Mathematics and Technology
- Hardcover
Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
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Language: English
Published by Springer, 2025
Series: Book 32 of 32 - Springer Undergraduate Texts in Mathematics and Technology
- Hardcover
Seller: California Books, Miami, FL, U.S.A.California Books
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Published by Springer
- Hardcover
Seller: Academic Book Solutions, Medford, NY, U.S.A.Academic Book Solutions
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Add to baskethardcover. Condition: VeryGood. A copy that may have been read, very minimal wear and tear. May have a remainder mark.
Language: English
Published by Springer, 2025
Series: Book 32 of 32 - Springer Undergraduate Texts in Mathematics and Technology
- Hardcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Condition: As New. Unread book in perfect condition.
Language: English
Published by Springer, 2025
Series: Book 32 of 32 - Springer Undergraduate Texts in Mathematics and Technology
- Hardcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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Language: English
Published by Springer International Publishing AG, CH, 2025
Series: Book 32 of 32 - Springer Undergraduate Texts in Mathematics and Technology
- Hardcover
Seller: Rarewaves USA, OSWEGO, IL, U.S.A.Rarewaves USA
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Hardback. Condition: New.
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Condition: New. Print on Demand. King, Lisa (illustrator).
Language: English
Published by Springer, 2025
Series: Book 32 of 32 - Springer Undergraduate Texts in Mathematics and Technology
- Hardcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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Condition: As New. Unread book in perfect condition.
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Language: English
Published by Springer International Publishing AG, CH, 2025
Series: Book 32 of 32 - Springer Undergraduate Texts in Mathematics and Technology
- Hardcover
Seller: Rarewaves.com USA, London, LONDO, United KingdomRarewaves.com USA
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Hardback. Condition: New. 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 notebooks 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.
Language: English
Published by Springer, 2025
Series: Book 32 of 32 - Springer Undergraduate Texts in Mathematics and Technology
- Hardcover
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrelandKennys Bookshop and Art Galleries Ltd.
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- Hardcover
- First Edition
Seller: Nightshade Booksellers, IOBA member, Atlanta, GA, U.S.A.Nightshade Booksellers, IOBA member
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Condition: Used - Fine
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Hardcover. Condition: Fine. 1st Edition. First edition. A fine copy in a fine slipcase. A fabulous book with BMWs interpreted by many iconic artists. See my photos of the book you will receive, not stock photos. More available upon request. This book is in my possession and will be packed in bubble wrap and shipped in a cardboar…d box. USPS tracking provided. #140. Andy Warhol, Alexander Calder, David Hockney, Jeff Koons, Roy Lichtenstein, et al (illustrator).
Language: English
Published by Springer, 2025
Series: Book 32 of 32 - Springer Undergraduate Texts in Mathematics and Technology
- Hardcover
Seller: Kennys Bookstore, Olney, MD, U.S.A.Kennys Bookstore
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US$ 141.94
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Language: English
Published by Springer Nature, 2025
Series: Book 32 of 32 - Springer Undergraduate Texts in Mathematics and Technology
- Hardcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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US$ 141.89
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Hardcover. Condition: Brand New. 652 pages. 10.00x7.00x10.00 inches. In Stock.
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Language: English
Published by Springer International Publishing AG, CH, 2025
Series: Book 32 of 32 - Springer Undergraduate Texts in Mathematics and Technology
- Hardcover
Seller: Rarewaves USA United, OSWEGO, IL, U.S.A.Rarewaves USA United
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US$ 120.63
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Hardback. Condition: New. 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 notebooks 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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Language: English
Published by Springer, 2025
Series: Book 32 of 32 - Springer Undergraduate Texts in Mathematics and Technology
- Hardcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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US$ 95.69
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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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Language: English
Published by Springer International Publishing AG, CH, 2025
Series: Book 32 of 32 - Springer Undergraduate Texts in Mathematics and Technology
- Hardcover
Seller: Rarewaves.com UK, London, United KingdomRarewaves.com UK
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US$ 121.19
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Hardback. Condition: New.
- Softcover
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Seller: THE SAINT BOOKSTORE, Southport, United KingdomTHE SAINT BOOKSTORE
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US$ 30.14
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Paperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days. King, Lisa (illustrator).
Language: English
Published by Springer, 2025
Series: Book 32 of 32 - Springer Undergraduate Texts in Mathematics and Technology
- Hardcover
- Print on Demand
Seller: Brook Bookstore On Demand, Napoli, NA, ItalyBrook Bookstore On Demand
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Condition: new. Questo è un articolo print on demand.
Language: English
Published by Springer, 2025
Series: Book 32 of 32 - Springer Undergraduate Texts in Mathematics and Technology
- Hardcover
- Print on Demand
Seller: Basi6 International, Irving, TX, U.S.A.Basi6 International
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Condition: Brand New. New. US edition. Print on demand title. Delivery takes 20-25 days. Excellent Customer Service.
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Language: English
Published by Springer, Springer Aug 2025, 2025
Series: Book 32 of 32 - Springer Undergraduate Texts in Mathematics and Technology
- Hardcover
- Print on Demand
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
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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 prerequi…sites. 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.
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Language: English
Published by Springer Verlag GmbH, 2025
Series: Book 32 of 32 - Springer Undergraduate Texts in Mathematics and Technology
- Hardcover
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Seller: moluna, Greven, Germanymoluna
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.
Language: English
Published by Springer International Publishing AG, Cham, 2025
Series: Book 32 of 32 - Springer Undergraduate Texts in Mathematics and Technology
- Hardcover
- Print on Demand
Seller: CitiRetail, Stevenage, United KingdomCitiRetail
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US$ 98.71
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Hardcover. Condition: new. Hardcover. 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 wh…y 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 notebooks 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 texts 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Language: English
Published by Springer, Springer Aug 2025, 2025
Series: Book 32 of 32 - Springer Undergraduate Texts in Mathematics and Technology
- Hardcover
- Print on Demand
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
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Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. 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 prerequisite…s. 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 656 pp. Englisch.
