Linear algebra and optimization with applications to machine learning: linear algebra for computer vision, robotics, and machine learning

By:

Jean Gallier

Contributor(s):

Quaintance, Jocelyn

Material type: Text

TextPublication details: World Scientific Publishing Singapore 2023Description: xv, 806 pISBN:

9781944660345

Subject(s):

Algebras, Linear

DDC classification:

512.5 JEA

Summary: This book provides the mathematical fundamentals of linear algebra to practicers in computer vision, machine learning, robotics, applied mathematics, and electrical engineering. By only assuming a knowledge of calculus, the authors develop, in a rigorous yet down to earth manner, the mathematical theory behind concepts such as: vectors spaces, bases, linear maps, duality, Hermitian spaces, the spectral theorems, SVD, and the primary decomposition theorem. At all times, pertinent real-world applications are provided. This book includes the mathematical explanations for the tools used which we believe that is adequate for computer scientists, engineers and mathematicians who really want to do serious research and make significant contributions in their respective fields. (https://www.worldscientific.com/worldscibooks/10.1142/11446#t=aboutBook)

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Holdings
Item type	Current library	Collection	Call number	Vol info	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	Operations Management & Quantitative Techniques	512.5 JEA (Browse shelf(Opens below))	Vol. I	1	Available		006402

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Previous	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	512.5 LUE Linear and nonlinear programming	Next

This book provides the mathematical fundamentals of linear algebra to practicers in computer vision, machine learning, robotics, applied mathematics, and electrical engineering. By only assuming a knowledge of calculus, the authors develop, in a rigorous yet down to earth manner, the mathematical theory behind concepts such as: vectors spaces, bases, linear maps, duality, Hermitian spaces, the spectral theorems, SVD, and the primary decomposition theorem. At all times, pertinent real-world applications are provided. This book includes the mathematical explanations for the tools used which we believe that is adequate for computer scientists, engineers and mathematicians who really want to do serious research and make significant contributions in their respective fields.

(https://www.worldscientific.com/worldscibooks/10.1142/11446#t=aboutBook)

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