Linear Algebra For Data Science
Regular price
€82.99
A01=Moshe Haviv
Author_Moshe Haviv
Category=PBD
Category=PBF
Determinates
Diagonalizability
Eigensystems
Eigenvalues
eq_isMigrated=1
eq_nobargain
Invertible Matrices
Linear Functions
Linear Independence
Linear Subspaces
Matrices
Matrix Inversion
Matrix Operations
Orthonormal Bases
Projections
Pseudo-Inverse
Regression
Singular Value Decomposition
Stochastic Matrices
Symmetric Matrices
The Gram-Schmidt Process
Vector Algebra
Product details
- ISBN 9789811276224
- Publication Date: 18 Jul 2023
- Publisher: World Scientific Publishing Co Pte Ltd
- Publication City/Country: SG
- Product Form: Hardback
Secure
checkout
Fast Shipping
Easy returns
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.
