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

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Authors

Jeffrey A. Fessler
, University of Michigan, Ann Arbor,
Raj Rao Nadakuditi
, University of Michigan, Ann Arbor

Description

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…

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Key features

  • Engages students with interesting applications in data science, machine learning and signal processing
  • Encourages active learning with over 100 engaging 'explore' problems, with answers at the back of each chapter
  • Contains over 200 questions suitable for in-class interactive learning or quizzes, developed and used in the authors' own courses
  • Provides numerous Julia code examples and a suite of computational notebook demos offering a hands-on learning experience for students

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