Linear Algebra and Optimization for Machine Learning : A Textbook /

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...

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Bibliographic Details
Main Author:	Aggarwal, Charu C. (Author)
Corporate Author:	SpringerLink (Online service)
Format:	eBook
Language:	English
Published:	Cham : Springer International Publishing : Imprint: Springer, 2020.
Edition:	1st ed. 2020.
Subjects:	Machine learning. Matrix theory. Algebra. Computers. Machine Learning. Linear and Multilinear Algebras, Matrix Theory. Information Systems and Communication Service. Electronic books.
Online Access:	Connect to the full text of this electronic book

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.