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

by Raj Rao Nadakuditi and Jeffrey A. Fessler

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,...

• Format: Hardcover
• Language: English
• ISBN: 1009418149
• ISBN13: 9781009418140
• Release Date: July 2024
• Publisher: Cambridge University Press
• Length: 450 Pages
• Weight: 2.05 lbs.
• Dimensions: 1.2" x 6.8" x 9.7"