Coding the Matrix: Linear Algebra through Computer Science Applications

by Philip N. Klein

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"

Genres: Mathematics, Computer Science, Programming, Computers, Textbooks, Science, Nonfiction

528 pages, Paperback

First published July 26, 2013

Reviews

[Gavin · Rating 4 out of 5]

Clear, friendly, with computation and maths given equal footing. The XKCD humour gets old but this is the cost of complete sentences and general friendliness.

He could have gone further with the type-signature approach to maths definitions than he does.

[Randy · Rating 4 out of 5]

Read through during first session of Coursera course on linear algebra - about to have another go when the class is repeated. So happy the examples are in Phython

[Matthew Talbert · Rating 5 out of 5]

Very good introduction to linear algebra, particularly if you can write code in a modern language like Python.

[David · Rating 4 out of 5]

I work with a few linear algebra types, so I decided a refresher was in order. Many years ago in college, the course I took worked up through idea of the Gram-Schmidt orthonomalizing process. This means I paid some of my dues, but stopping just shy of where the useful concepts start to kick in (processing data in a smaller/sparse basis, PCA, etc.). I selected this book because of its use of Python, and slowly worked through the content (however, I didn't code up the exercises). The FFT explanation is quite good. It was nice to see the author clearly state that determinants are good for making mathematical arguments, but are rarely useful in computation. That seems consistent with much in linear algebra, as it's not uncommon for me to be reading an algorithm paper that leads with a linear algebra explanation, but then goes on to say that it could never be computed using such an approach, and falls back on something more practical.

[Meremi · Rating 5 out of 5]

Great

"What is twice read, is commonly better remembered than what is transcribed." Dr. Johnson
Play by the rules. Hard work, patience, and persistence bring personal insight and freedom.
Peace

[Pierre Jacomet · Rating 1 out of 5]

DNF - I hate to give a 1 star, especially when this is NOT related to the content, but rather specific to the Kindle edition. Yet, I find no other way to provide some indication to any readers of the Kindle edition of this book, that it is very bad. It's bad enough that it is just a scan of the printed version into Kindle, but to make things worse, you cannot highlight lines of text, other than in the first page of chapters. This makes the Kindle edition unusable for studying for me.

If I am able to get a better digital version I will resume reading this book, otherwise it's a goner for me as reading in paper is too 1990s.

[Luis Wenus · Rating 2 out of 5]

Got this recommended to catch up on manths/coding but expected a much higher level, this is first-year university maths. Also doesn't use numpy which is weird for a maths in python-book. Nicely written though.

[Dien Dang · Rating 5 out of 5]

Excellent book about linear algebra, especially about its application, the book covers almost all level of linear algebra, suitable for both beginner and experienced. Also, have a much practical code example to understand them deeply.