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Linear algebra and optimization for machine learning: a textbook

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This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout the book. A solution manual for the exercises at the end of each chapter is available to teaching instructors. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced undergraduate students can also use this textbook.

Table of Contents

1. 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 Graph

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Detail Information

Series Title

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Call Number

512.5 AGG l

Publisher

Switzerland : Springer., 2020

Collation

xxi, 495 hlm.; ilus.; 26 cm

Language

English

ISBN/ISSN

9783030403430

Classification

512.5

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Algebras, Linear
Machine - Learning

Specific Detail Info

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Statement of Responsibility

Charu C. Aggarwal

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