9780615880990-0615880991-Coding the Matrix: Linear Algebra through Applications to Computer Science

Coding the Matrix: Linear Algebra through Applications to Computer Science

ISBN-13: 9780615880990
ISBN-10: 0615880991
Edition: 8.4.2013
Author: Philip N. Klein
Publication date: 2013
Publisher: Newtonian Press
Format: Paperback 548 pages
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Book details

ISBN-13: 9780615880990
ISBN-10: 0615880991
Edition: 8.4.2013
Author: Philip N. Klein
Publication date: 2013
Publisher: Newtonian Press
Format: Paperback 548 pages

Summary

Coding the Matrix: Linear Algebra through Applications to Computer Science (ISBN-13: 9780615880990 and ISBN-10: 0615880991), written by authors Philip N. Klein, was published by Newtonian Press in 2013. With an overall rating of 4.0 stars, it's a notable title among other Computer Science (Applied, Mathematics, Matrices) books. You can easily purchase or rent Coding the Matrix: Linear Algebra through Applications to Computer Science (Paperback) from BooksRun, along with many other new and used Computer Science books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $5.49.

Description

An engaging introduction to vectors and matrices and the algorithms that operate on them, intended for the student who knows how to program. Mathematical concepts and computational problems are motivated by applications in computer science. The reader learns by doing, writing programs to implement the mathematical concepts and using them to carry out tasks and explore the applications. Examples include: error-correcting codes, transformations in graphics, face detection, encryption and secret-sharing, integer factoring, removing perspective from an image, PageRank (Google's ranking algorithm), and cancer detection from cell features. A companion web site, codingthematrix.com provides data and support code. Most of the assignments can be auto-graded online. Over two hundred illustrations, including a selection of relevant xkcd comics.

Chapters: The Function, The Field, The Vector, The Vector Space, The Matrix, The Basis, Dimension, Gaussian Elimination, The Inner Product, Special Bases, The Singular Value Decomposition, The Eigenvector, The Linear Program

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