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Matrix Methods in Data Analysis
Hardcover
Series: Texts in Applied Mathematics, Book 80
AlgebraGeneral ComputersProbability & Statistics
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ISBN10: 303211313X
ISBN13: 9783032113139
Publisher: Springer
Published: Sep 23 2026
Pages: 1004
Language: English
ISBN13: 9783032113139
Publisher: Springer
Published: Sep 23 2026
Pages: 1004
Language: English
This textbook offers a fresh and balanced approach to the study of Linear Algebra in the context of modern Data Science. Whereas many existing texts either emphasize theory with little connection to practice or jump straight to applications with minimal mathematical explanation, this book provides equal weight to both foundations and applications.
Designed for undergraduates who have completed a proof-based Linear Algebra course, it introduces concepts and tools from Matrix Analysis that are essential for Data Science and Machine Learning. Topics include:
- Vector norms and distances, orthogonality, and projections Matrix factorizations such as LU, CR, QR, and SVD Special matrix types: symmetric, positive definite, nonnegative, stochastic, and covariance matrices Key numerical algorithms, including the QR algorithm and the Power Method
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