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Linear algebra for data science

By:

Haviv, Moshe

Material type: Text

TextPublication details: World Scientific Publishing Singapore 2024Description: xii, 244 pISBN:

9798886130775

Subject(s):

DDC classification:

512.5 HAV

Summary: This book serves as an introduction to linear algebra for undergraduate students in data science, statistics, computer science, economics, and engineering. The book presents all the essentials in rigorous (proof-based) manner, describes the intuition behind the results, while discussing some applications to data science along the way. The book comes with two parts, one on vectors, the other on matrices. The former consists of four chapters: vector algebra, linear independence and linear subspaces, orthonormal bases and the Gram–Schmidt process, linear functions. The latter comes with eight chapters: matrices and matrix operations, invertible matrices and matrix inversion, projections and regression, determinants, eigensystems and diagonalizability, symmetric matrices, singular value decomposition, and stochastic matrices. The book ends with the solution of exercises which appear throughout its twelve chapters. (https://www.worldscientific.com/worldscibooks/10.1142/13408#t=aboutBook)

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	Operations Management & Quantitative Techniques	512.5 HAV (Browse shelf(Opens below))	1	Available		006405

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Previous	512.5 ANT Elementary linear algebra: with supplemental applications	512.5 BRE Linear algebra with applications	512.5 DRA Linear algebra: core topics for the second course	512.5 HAV Linear algebra for data science	512.5 JEA Linear algebra and optimization with applications to machine learning: linear algebra for computer vision, robotics, and machine learning	512.5 JOH Introduction to linear and matrix algebra	512.5 KOL Elementary linear algebra with applications	Next

This book serves as an introduction to linear algebra for undergraduate students in data science, statistics, computer science, economics, and engineering. The book presents all the essentials in rigorous (proof-based) manner, describes the intuition behind the results, while discussing some applications to data science along the way.

The book comes with two parts, one on vectors, the other on matrices. The former consists of four chapters: vector algebra, linear independence and linear subspaces, orthonormal bases and the Gram–Schmidt process, linear functions. The latter comes with eight chapters: matrices and matrix operations, invertible matrices and matrix inversion, projections and regression, determinants, eigensystems and diagonalizability, symmetric matrices, singular value decomposition, and stochastic matrices. The book ends with the solution of exercises which appear throughout its twelve chapters.

(https://www.worldscientific.com/worldscibooks/10.1142/13408#t=aboutBook)

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