Linear Algebra, Data Science, and Machine Learning

Calder, Jeff; Olver, Peter J.

ISBN 10: 3031937635 ISBN 13: 9783031937637
Verlag: Springer, 2025
Neu HRD

Verkäufer PBShop.store UK, Fairford, GLOS, Vereinigtes Königreich Verkäuferbewertung 4 von 5 Sternen 4 Sterne, Erfahren Sie mehr über Verkäufer-Bewertungen

Heritage Bookseller
AbeBooks-Mitglied seit 1996


Beschreibung

Beschreibung:

New Book. Shipped from UK. Established seller since 2000. Bestandsnummer des Verkäufers GB-9783031937637

Diesen Artikel melden

Inhaltsangabe:

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.

Über die Autorin bzw. den Autor:

Jeff Calder received his Ph.D. degree in applied and interdisciplinary mathematics from the University of Michigan under the guidance of Prof. Selim Esedoglu and Prof. Alfred Hero in 2014. Between 2014 and 2016 he was a Morrey Assistant Professor at the University of California, Berkeley, under the mentorship of Lawrence C. Evans and James Sethian. He has been on the faculty of the School of Mathematics at the University of Minnesota since 2016, full professor since 2025, where he has supervised 5 PhD students, 4 postdoctoral scholars, and a number of undergraduate and high school students on research projects. 


Calder's research interests lie in applied probability, numerical analysis, and partial differential equations, with a specific interest in applications to machine learning and data analysis. Calder has published over 50 articles in journals and conferences spanning pure and applied mathematics and related areas, and holds several patents. His research has been recognized with an NSF Career Award and Alfred P. Sloan Research Fellowship in 2020, a University of Minnesota McKnight Presidential Fellowship and Guillermo E. Borja Award in 2021, and he currently holds the Albert and Dorothy Marden Professorship in Mathematics (2023-2028).

Peter J. Olver received his Ph.D. from Harvard University in 1976 under the guidance of Prof. Garrett Birkhoff.  After being a Dickson Instructor at the University of Chicago and a postdoc at the University of Oxford, he has been on the faculty of the School of Mathematics at the University of Minnesota since 1980, and a full professor since 1985.  He served as the Head of the Department from 2008 to 2020.  He has supervised 23 Ph.D. students, and mentored over 30 postdocs, visiting students and scholars from around the world, as well as supervising numerous undergraduate research projects.  He is a Fellow of the American Mathematical Society, the Society for Industrial and Applied Mathematics (SIAM), the Institute of Physics, UK, and the Asia-Pacific Artificial Intelligence Association (AAIA).   


Over the years, he has contributed to a wide range of fields, including symmetry and Lie theory, partial differential equations, the calculus of variations, mathematical physics, fluid mechanics, elasticity, quantum mechanics, Hamiltonian mechanics, geometric numerical methods, differential geometry, classical invariant theory, algebra, computer vision and image processing, anthropology, and beyond. He is the author of over 160 papers in refereed journals, and has given more than 500 invited lectures on his research at conferences, universities, colleges, and institutes throughout the world.  He was named a "Highly Cited Researcher” by Thomson-ISI in 2003, and an inaugural "Highly Ranked Scholar" by ScholarGPS in 2024..  He has written 6 books, including the definitive text on Applications of Lie Groups to Differential Equations, and two additional undergraduate texts: Partial Differential Equations and Applied Linear Algebra, the latter coauthored with his wife, Chehrzad Shakiban.

„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.

Bibliografische Details

Titel: Linear Algebra, Data Science, and Machine ...
Verlag: Springer
Erscheinungsdatum: 2025
Einband: HRD
Zustand: New

Beste Suchergebnisse beim ZVAB

Beispielbild für diese ISBN

Calder, Jeff,Olver, Peter J.
Verlag: Springer, 2025
ISBN 10: 3031937635 ISBN 13: 9783031937637
Gebraucht Hardcover

Anbieter: Books From California, Simi Valley, CA, USA

Verkäuferbewertung 4 von 5 Sternen 4 Sterne, Erfahren Sie mehr über Verkäufer-Bewertungen

hardcover. Zustand: Very Good. Artikel-Nr. mon0003966299

Verkäufer kontaktieren

Gebraucht kaufen

EUR 52,07
EUR 4,23 Versand
Versand innerhalb von USA

Anzahl: 1 verfügbar

In den Warenkorb

Beispielbild für diese ISBN

Calder, Jeff,Olver, Peter J.
Verlag: Springer, 2025
ISBN 10: 3031937635 ISBN 13: 9783031937637
Gebraucht Hardcover

Anbieter: Books From California, Simi Valley, CA, USA

Verkäuferbewertung 4 von 5 Sternen 4 Sterne, Erfahren Sie mehr über Verkäufer-Bewertungen

hardcover. Zustand: Good. Artikel-Nr. mon0003965116

Verkäufer kontaktieren

Gebraucht kaufen

EUR 52,07
EUR 4,23 Versand
Versand innerhalb von USA

Anzahl: 3 verfügbar

In den Warenkorb

Beispielbild für diese ISBN

Calder, Jeff,Olver, Peter J.
Verlag: Springer, 2025
ISBN 10: 3031937635 ISBN 13: 9783031937637
Gebraucht Hardcover

Anbieter: Books From California, Simi Valley, CA, USA

Verkäuferbewertung 4 von 5 Sternen 4 Sterne, Erfahren Sie mehr über Verkäufer-Bewertungen

hardcover. Zustand: Fine. Artikel-Nr. mon0004098532

Verkäufer kontaktieren

Gebraucht kaufen

EUR 52,07
EUR 4,23 Versand
Versand innerhalb von USA

Anzahl: 1 verfügbar

In den Warenkorb

Beispielbild für diese ISBN

Calder, Jeff; Olver, Peter J.
Verlag: Springer, 2025
ISBN 10: 3031937635 ISBN 13: 9783031937637
Neu Hardcover

Anbieter: Majestic Books, Hounslow, Vereinigtes Königreich

Verkäuferbewertung 4 von 5 Sternen 4 Sterne, Erfahren Sie mehr über Verkäufer-Bewertungen

Zustand: New. Artikel-Nr. 409011608

Verkäufer kontaktieren

Neu kaufen

EUR 72,75
EUR 7,52 Versand
Versand von Vereinigtes Königreich nach USA

Anzahl: 2 verfügbar

In den Warenkorb

Foto des Verkäufers

Calder, Jeff/Olver, Peter J
Verlag: Springer Verlag GmbH, 2025
ISBN 10: 3031937635 ISBN 13: 9783031937637
Neu Hardcover

Anbieter: moluna, Greven, Deutschland

Verkäuferbewertung 5 von 5 Sternen 5 Sterne, Erfahren Sie mehr über Verkäufer-Bewertungen

Zustand: New. Artikel-Nr. 2345704831

Verkäufer kontaktieren

Neu kaufen

EUR 73,02
EUR 48,99 Versand
Versand von Deutschland nach USA

Anzahl: 1 verfügbar

In den Warenkorb

Beispielbild für diese ISBN

Calder, Jeff; Olver, Peter J.
Verlag: Springer, 2025
ISBN 10: 3031937635 ISBN 13: 9783031937637
Neu Hardcover

Anbieter: Romtrade Corp., STERLING HEIGHTS, MI, USA

Verkäuferbewertung 5 von 5 Sternen 5 Sterne, Erfahren Sie mehr über Verkäufer-Bewertungen

Zustand: 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. Artikel-Nr. ABBB-25440

Verkäufer kontaktieren

Neu kaufen

EUR 74,41
Versand gratis
Versand innerhalb von USA

Anzahl: 5 verfügbar

In den Warenkorb

Foto des Verkäufers

Jeff Calder
Verlag: Springer, Springer, 2025
ISBN 10: 3031937635 ISBN 13: 9783031937637
Neu Hardcover

Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland

Verkäuferbewertung 5 von 5 Sternen 5 Sterne, Erfahren Sie mehr über Verkäufer-Bewertungen

Buch. Zustand: 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. Artikel-Nr. 9783031937637

Verkäufer kontaktieren

Neu kaufen

EUR 74,89
EUR 67,05 Versand
Versand von Deutschland nach USA

Anzahl: 1 verfügbar

In den Warenkorb

Beispielbild für diese ISBN

Calder, Jeff/ Olver, Peter J.
Verlag: Springer Nature, 2025
ISBN 10: 3031937635 ISBN 13: 9783031937637
Neu Hardcover

Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich

Verkäuferbewertung 5 von 5 Sternen 5 Sterne, Erfahren Sie mehr über Verkäufer-Bewertungen

Hardcover. Zustand: Brand New. 652 pages. 10.00x7.00x10.00 inches. In Stock. Artikel-Nr. x-3031937635

Verkäufer kontaktieren

Neu kaufen

EUR 120,93
EUR 17,34 Versand
Versand von Vereinigtes Königreich nach USA

Anzahl: 2 verfügbar

In den Warenkorb

Beispielbild für diese ISBN

Calder, Jeff
Verlag: Springer, 2025
ISBN 10: 3031937635 ISBN 13: 9783031937637
Neu Hardcover

Anbieter: Kennys Bookstore, Olney, MD, USA

Verkäuferbewertung 5 von 5 Sternen 5 Sterne, Erfahren Sie mehr über Verkäufer-Bewertungen

Zustand: New. Artikel-Nr. V9783031937637

Verkäufer kontaktieren

Neu kaufen

EUR 124,70
EUR 8,91 Versand
Versand innerhalb von USA

Anzahl: 1 verfügbar

In den Warenkorb