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Linear Algebra for Data Science, Machine Learning, and Signal Processing

Fessler, Jeffrey A.,Nadakuditi, Raj Rao

Published by Cambridge University Press, 2024

ISBN 10: 1009418149 ISBN 13: 9781009418140

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hardcover. Condition: Very Good. Cover and edges may have some wear. Seller Inventory # mon0003660315

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Linear Algebra for Data Science, Machine Learning, and Signal Processing

Fessler, Jeffrey A.; Nadakuditi, Raj Rao

Published by Cambridge University Press, 2024

ISBN 10: 1009418149 ISBN 13: 9781009418140

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Linear Algebra for Data Science, Machine Learning, and Signal Processing

Fessler, Jeffrey A.

Published by Cambridge University Press, 2024

ISBN 10: 1009418149 ISBN 13: 9781009418140

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Linear Algebra for Data Science, Machine Learning, and Signal Processing

Fessler, Jeffrey A.; Nadakuditi, Raj Rao

Published by Cambridge University Press, 2024

ISBN 10: 1009418149 ISBN 13: 9781009418140

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Linear Algebra for Data Science, Machine Learning, and Signal Processing

Fessler, Jeffrey A.

Published by Cambridge University Press, 2024

ISBN 10: 1009418149 ISBN 13: 9781009418140

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Linear Algebra for Data Science, Machine Learning, and Signal Processing

Fessler, Jeffrey A.; Nadakuditi, Raj Rao

Published by Cambridge University Press, 2024

ISBN 10: 1009418149 ISBN 13: 9781009418140

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Linear Algebra for Data Science, Machine Learning, and Signal Processing (Hardcover)

Jeffrey A. Fessler

Published by Cambridge University Press, Cambridge, 2024

ISBN 10: 1009418149 ISBN 13: 9781009418140

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Hardcover. Condition: new. Hardcover. Maximise student engagement and understanding of matrix methods in data-driven applications with this modern teaching package. Students are introduced to matrices in two preliminary chapters, before progressing to advanced topics such as the nuclear norm, proximal operators and convex optimization. Highlighted applications include low-rank approximation, matrix completion, subspace learning, logistic regression for binary classification, robust PCA, dimensionality reduction and Procrustes problems. Extensively classroom-tested, the book includes over 200 multiple-choice questions suitable for in-class interactive learning or quizzes, as well as homework exercises (with solutions available for instructors). It encourages active learning with engaging 'explore' questions, with answers at the back of each chapter, and Julia code examples to demonstrate how the mathematics is actually used in practice. A suite of computational notebooks offers a hands-on learning experience for students. This is a perfect textbook for upper-level undergraduates and first-year graduate students who have taken a prior course in linear algebra basics. Master basic matrix methods by seeing how the mathematics is used in practice in a range of data-driven applications. Includes a wealth of engaging exercises for quizzes, self-study and interactive learning, as well as online JULIA demos offering a hands-on learning experience for upper-level undergraduates and first-year graduate students. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781009418140

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