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Introduction To Linear Algebra

Mark J. DeBonis
€107.99
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Additive Inverse
Algebra
Applications
Author_Mark J. DeBonis
BDC
Category=PBF
Category=PBUH
Category=PBW
Category=UYA
computational algebra
Computations
data analytics
Data Set
Data Sphering
Dimensional Data
Dimensional Plot
eigenvalue analysis
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Feature Space
Fisher LDF
Gaussian elimination
Initial Point
LDF
Linear
Linear Algebra
linear algebraic computations
machine learning
Mahalanobis Distance
Markov chains
Matrix Multiplication
Non-zero Singular Values
Orthogonal Matrix
orthogonal projections
PCA Direction
principal component analysis applications
Prove Properties
QR factorization
Scatter Matrix
simplex optimization
Skew Symmetric Matrix
Square Matrix
Standard Basis Vectors
statistics
Terminal Point
textbook
Truncated SVD
Truncated SVD Method
Unit Vector Parallel
Vector Space

Product details

  • ISBN 9781032108988
  • Weight: 866g
  • Dimensions: 178 x 254mm
  • Publication Date: 15 Mar 2022
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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Description

Introduction to Linear Algebra: Computation, Application, and Theory is designed for students who have never been exposed to the topics in a linear algebra course. The text is filled with interesting and diverse application sections but is also a theoretical text which aims to train students to do succinct computation in a knowledgeable way. After completing the course with this text, the student will not only know the best and shortest way to do linear algebraic computations but will also know why such computations are both effective and successful.

Features:

  • Includes cutting edge applications in machine learning and data analytics

  • Suitable as a primary text for undergraduates studying linear algebra

  • Requires very little in the way of pre-requisites
About Mark J. DeBonis

Mark J. DeBonis received his PhD in Mathematics from the University of California, Irvine, USA. He began his career as a theoretical mathematician in the field of group theory and model theory, but in later years switched to applied mathematics, in particular to machine learning. He spent some time working for the US Department of Energy at Los Alamos National Lab as well as the US Department of Defense at the Defense Intelligence Agency as an applied mathematician of machine learning. He is an Associate Professor of Mathematics at Manhattan College in New York City and is also currently working for the US Department of Energy at Sandia National Lab as a Principal Data Analyst. His research interests include machine learning, statistics, and computational algebra.

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