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Linear Algebra and Optimization for Machine Learning A Textbook

The tight integration of linear algebra methods with examples from machine learning differentiates this book fromgeneric volumes on linear algebra. The focus is clearly on the most relevant aspects of linear algebra for machine learning and to teach readers how to apply these concepts. 2. Optimizati...

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Bibliographic Details
Main Author: Aggarwal, Charu C.
Format: eBook
Language:English
Published: Cham Springer International Publishing 2020, 2020
Edition:1st ed. 2020
Subjects:
Computer Communication Networks
Machine Learning
Computer Networks 
Linear Algebra
Algebras, Linear
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
Table of Contents:
  • Preface
  • 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 Graphs
  • Index

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