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Cambridge University Press Cambridge Linear Algebra for Data Science and Machine Learning

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Description

Master matrix methods with this modern teaching package from Cambridge University Press. Designed to increase student engagement, this book moves from fundamental matrix introductions to advanced topics like the nuclear norm, proximal operators, and convex optimization. It provides practical insights into data-driven applications including low-rank approximation, matrix completion, and subspace learning. Students will explore real-world use cases such as logistic regression for binary classification, robust PCA, dimensionality reduction, and Procrustes problems. This extensively classroom-tested resource is built to support deep understanding in signal processing and machine learning environments. With over 200 multiple-choice questions included, it serves as a comprehensive tool for both instructors and learners in engineering and mathematics.

Key Features

Cambridge University Press provides this modern teaching package designed to improve understanding of matrix methods in data-driven applications.

The curriculum covers advanced mathematical topics including the nuclear norm, proximal operators, and convex optimization.

Practical applications featured in the text include low-rank approximation, matrix completion, subspace learning, and robust PCA.

Learn essential techniques for data science such as logistic regression for binary classification and dimensionality reduction.

This classroom-tested book includes over 200 multiple-choice questions to support student assessment and learning.

The content addresses complex mathematical problems like Procrustes problems to enhance signal processing expertise.

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