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Linear algebra and optimization for machine learning: a textbook/ Charu C. Aggarwal

By: Publication details: Switzerland: Springer, 2020Description: xxi,495 pages, 25 cmISBN:
  • 9783030403461
Subject(s): DDC classification:
  • 23 512.5 Ag266
Contents:
Linear algebra and optimization : an introduction -- 2. Linear transformations and linear systems -- 3. Eigenvectors and diagonalizable matrices -- 4. Optimization basics: a machine learning view -- 5. Advanced optimization solutions -- 6. Constrained optimization and duality -- 7. Singular value decomposition -- 8. Matrix factorization -- 9. The Linear algebra of similarity -- 10. The Linear algebra of graphs -- 11. Optimization in computational graphs
Summary: This textbook provides an integrated treatment of linear algebra and optimization with a special focus on machine learning issues. It Includes many examples to simplify exposition and facilitate in learning semantically. It is complemented by examples and exercises throughout the book. A solution manual for the exercises at the end of each chapter is available to teaching instructors
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Includes bibliographical references and index

Linear algebra and optimization : an introduction -- 2. Linear transformations and linear systems -- 3. Eigenvectors and diagonalizable matrices -- 4. Optimization basics: a machine learning view -- 5. Advanced optimization solutions -- 6. Constrained optimization and duality -- 7. Singular value decomposition -- 8. Matrix factorization -- 9. The Linear algebra of similarity -- 10. The Linear algebra of graphs -- 11. Optimization in computational graphs

This textbook provides an integrated treatment of linear algebra and optimization with a special focus on machine learning issues.
It Includes many examples to simplify exposition and facilitate in learning semantically. It is complemented by examples and exercises throughout the book. A solution manual for the exercises at the end of each chapter is available to teaching instructors

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