Jeff calder (31 risultati)
American Art in the 20th Century : Painting and Sculpture, 1913 - 1993 [Paperback / English Edition]
Lingua: Inglese
Editore: Royal Academy of Arts / Prestel-Verlag London / Munich, United Kingdom / Germany, 1993
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Da: Specific Object / David Platzker, New York, NY, U.S.A.Specific Object / David Platzker
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503 pp.; 29.8 x 22 cm.; sewn bound; black-and-white & color; edition size unknown; unsigned and unnumbered; offset-printed Exhibition catalogue published in conjunction with show held at the Martin-Gropius-Bau, Berlin, May 8 - July 25, 1993. Traveled to the Royal Academy of Arts and the Saatchi Gallery, London, September 16 - De…cember 12, 1993. Edited and with essays by Christos M. Joachimides, Norman Rosenthal, with co-ordinating editing by David Anfam. Additional essays by Brooks Adams, Richard Armstrong, John Beardsley, Neal Benezra, Achille Bonito Oliva, Arthur C. Danto, Abraham A. Davidson, Wolfgang Max Faust, Mary Emma Harris, Thomas Kellein, Donald Kuspit, Mary Lublin, Karal Ann Marling, Barbara Moore, Francis V. O'Connor, Stephen Polcari, Carter Ratcliff, Irving Sandler, Wieland Schmied, Peter Selz, Gail Stavitsky, and Douglas Tallack. Artists include Carl Andre, Richard Artschwager, Jean-Michel Basquiat, Jonathan Borofsky, James Lee Byars, Alexander Calder, John Chamberlain, Joseph Cornell, John Covert, Stuart Davis, Willem de Kooning, Charles Demuth, Arthur Dove, Marcel Duchamp, Dan Flavin, Sam Francis, Robert Gober, Arshile Gorky, Dan Graham, Philip Guston, David Hammons, Keith Haring, Marsden Hartley, Eva Hesse, Gary Hill, Jenny Holzer, Edward Hopper, Jasper Johns, Donald Judd, Mike Kelley, Ellsworth Kelly, Franz Kline, Jeff Koons, Sol LeWitt, Roy Lichtenstein, Robert Mangold, Brice Marden, Agnes Martin, Robert Morris, Gerald Murphy, Bruce Nauman, Barnett Newman, Georgia O'Keeffe, Claes Oldenburg, Jackson Pollock, Martin Puryear, Robert Rauschenberg, Man Ray, Ad Reinhardt, James Rosenquist, Mark Rothko, Edward Ruscha, Robert Ryman, Julian Schnabel, Richard Serra, Charles Sheeler, Cindy Sherman, David Smith, Frank Stella, Clyfford Still, James Turrell, Cy Twombly, Bill Viola, Andy Warhol, and Lawrence Weiner. Includes exhibition checklist, a list of artists in the exhibition, biographies of the artists, selected bibliography, author biographies, and an index of names. Text in English. Good. 5.5 cm. dog-ear to bottom right corner of recto and 9.5 cm. crease to top right corner of recto with bumping of corners. Light yellowing of pages. Contents clean and unmarked. Due to large size and weight additional shipping charges will be required for international orders.
Lingua: Inglese
Editore: Springer, 2025
Serie: Libro 32 di 32 - Springer Undergraduate Texts in Mathematics and Technology
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Da: Books From California, Simi Valley, CA, U.S.A.Books From California
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hardcover. Condizione: Good.
Lingua: Inglese
Editore: Springer, 2025
Serie: Libro 32 di 32 - Springer Undergraduate Texts in Mathematics and Technology
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Da: Books From California, Simi Valley, CA, U.S.A.Books From California
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hardcover. Condizione: Very Good.
Lingua: Inglese
Editore: Springer, 2025
Serie: Libro 32 di 32 - Springer Undergraduate Texts in Mathematics and Technology
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Da: Romtrade Corp., STERLING HEIGHTS, MI, U.S.A.Romtrade Corp.
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Condizione: 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.
Lingua: Inglese
Editore: Springer, 2025
Serie: Libro 32 di 32 - Springer Undergraduate Texts in Mathematics and Technology
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Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Serie: Libro 32 di 32 - Springer Undergraduate Texts in Mathematics and Technology
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Da: SMASS Sellers, IRVING, TX, U.S.A.SMASS Sellers
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Aggiungi al carrellohardcover. Condizione: VeryGood. A copy that may have been read, very minimal wear and tear. May have a remainder mark.
Lingua: Inglese
Editore: Springer, 2025
Serie: Libro 32 di 32 - Springer Undergraduate Texts in Mathematics and Technology
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Da: Books Puddle, New York, NY, U.S.A.Books Puddle
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Editore: Springer, 2025
Serie: Libro 32 di 32 - Springer Undergraduate Texts in Mathematics and Technology
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Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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Lingua: Inglese
Editore: Springer, 2025
Serie: Libro 32 di 32 - Springer Undergraduate Texts in Mathematics and Technology
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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Condizione: New.
Lingua: Inglese
Editore: Springer, 2025
Serie: Libro 32 di 32 - Springer Undergraduate Texts in Mathematics and Technology
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Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Lingua: Inglese
Editore: Springer, 2025
Serie: Libro 32 di 32 - Springer Undergraduate Texts in Mathematics and Technology
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Da: California Books, Miami, FL, U.S.A.California Books
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Serie: Libro 32 di 32 - Springer Undergraduate Texts in Mathematics and Technology
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Lingua: Inglese
Editore: Springer International Publishing AG, CH, 2025
Serie: Libro 32 di 32 - Springer Undergraduate Texts in Mathematics and Technology
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Da: Rarewaves USA, OSWEGO, IL, U.S.A.Rarewaves USA
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Hardback. Condizione: New.
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Da: California Books, Miami, FL, U.S.A.California Books
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Condizione: New. Print on Demand. King, Lisa (illustratore).
Lingua: Inglese
Editore: Springer, 2025
Serie: Libro 32 di 32 - Springer Undergraduate Texts in Mathematics and Technology
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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Lingua: Inglese
Editore: Springer International Publishing AG, CH, 2025
Serie: Libro 32 di 32 - Springer Undergraduate Texts in Mathematics and Technology
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Da: Rarewaves.com USA, London, LONDO, Regno UnitoRarewaves.com USA
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Hardback. Condizione: 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 algorithm…s 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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Da: Nightshade Booksellers, IOBA member, Atlanta, GA, U.S.A.Nightshade Booksellers, IOBA member
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Hardcover. Condizione: 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 cardboa…rd box. USPS tracking provided. #140. Andy Warhol, Alexander Calder, David Hockney, Jeff Koons, Roy Lichtenstein, et al (illustratore).
Lingua: Inglese
Editore: Springer, 2025
Serie: Libro 32 di 32 - Springer Undergraduate Texts in Mathematics and Technology
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Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrlandaKennys Bookshop and Art Galleries Ltd.
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Serie: Libro 32 di 32 - Springer Undergraduate Texts in Mathematics and Technology
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Editore: Springer Nature, 2025
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Hardcover. Condizione: Brand New. 652 pages. 10.00x7.00x10.00 inches. In Stock.
Lingua: Inglese
Editore: Springer, 2025
Serie: Libro 32 di 32 - Springer Undergraduate Texts in Mathematics and Technology
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Buch. Condizione: 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.
Lingua: Inglese
Editore: Springer International Publishing AG, CH, 2025
Serie: Libro 32 di 32 - Springer Undergraduate Texts in Mathematics and Technology
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Da: Rarewaves USA United, OSWEGO, IL, U.S.A.Rarewaves USA United
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EUR 105,49
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Hardback. Condizione: 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 algorithm…s 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.
Lingua: Inglese
Editore: Springer International Publishing AG, CH, 2025
Serie: Libro 32 di 32 - Springer Undergraduate Texts in Mathematics and Technology
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Da: Rarewaves.com UK, London, Regno UnitoRarewaves.com UK
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Hardback. Condizione: New.
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Paperback / softback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days. King, Lisa (illustratore).
Lingua: Inglese
Editore: Springer, 2025
Serie: Libro 32 di 32 - Springer Undergraduate Texts in Mathematics and Technology
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Da: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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Condizione: new. Questo è un articolo print on demand.
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Editore: Springer, Springer Aug 2025, 2025
Serie: Libro 32 di 32 - Springer Undergraduate Texts in Mathematics and Technology
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Buch. Condizione: 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 prerequ…isites. 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.
Lingua: Inglese
Editore: Springer Verlag GmbH, 2025
Serie: Libro 32 di 32 - Springer Undergraduate Texts in Mathematics and Technology
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Da: moluna, Greven, Germaniamoluna
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.
Lingua: Inglese
Editore: Springer International Publishing AG, Cham, 2025
Serie: Libro 32 di 32 - Springer Undergraduate Texts in Mathematics and Technology
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Hardcover. Condizione: 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 w…hy 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.
Lingua: Inglese
Editore: Springer, Springer Aug 2025, 2025
Serie: Libro 32 di 32 - Springer Undergraduate Texts in Mathematics and Technology
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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EUR 74,89
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Buch. Condizione: 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 prerequisit…es. 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.
