Fessler, Jeffrey A.
Linear Algebra for Data Science, Machine Learning, and Signal Processing
- ISBN 13:
- 9781009418140
- author:
- Fessler, Jeffrey A.
- format:
- Hardback
- publisher:
- Cambridge University Press
- language:
- English
- Publication Year:
- 2024
- Pages:
- 450
- Dimensions:
- 24.1 x 17.5 x 3.1 centimetres (0
- Genre:
- Computers
- Condition:
- New
- Availability:
- Item usually sent within 4 working days
Description
Linear Algebra for Data Science, Machine Learning, and Signal Processing by Jeffrey A. Fessler and Raj Rao Nadakuditi is a comprehensive textbook that introduces students to matrix methods in data-driven applications. The book builds upon the basics of linear algebra, covering advanced topics such as the nuclear norm, proximal operators, and convex optimization. The text features numerous examples and applications, including low-rank approximation, matrix completion, subspace learning, logistic regression for binary classification, robust PCA, dimensionality reduction, and Procrustes problems. To support active learning, the book includes over 200 multiple-choice questions, homework exercises with solutions, and Julia code examples to demonstrate practical applications. Suitable for upper-level undergraduates and first-year graduate students who have taken a prior course in linear algebra basics, this textbook offers a hands-on learning experience through a suite of computational notebooks.
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