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Coding the Matrix: Linear Algebra through Applications to Computer Science [Englisch] [Taschenbuch]

Philip N Klein

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Kurzbeschreibung

3. September 2013
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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Philip Klein is Professor of Computer Science at Brown University. He was a recipient of the National Science Foundation’s Presidential Young Investigator Award, and has received multiple research grants from the National Science Foundation. He has been made an ACM Fellow in recognition of his contributions to research on graph algorithms. He is a recipient of Brown University’s Award for Excellence in Teaching in the Sciences. Klein received a B.A. in Applied Mathematics from Harvard and a Ph.D. in Computer Science from MIT. He has been a Visiting Scientist at Princeton’s Computer Science Department, at MIT’s Mathematics Department, and at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), where he is currently a Research Affiliate. Klein has worked at industry research labs, including Xerox PARC and AT&T Labs, and he has been Chief Scientist at three start-ups. Klein was born and raised in Berkeley, California. He started learning programming in 1974, and started attending meetings of the Homebrew Computer Club a couple of years later. His love for computer science has never abated, but in a chance encounter with E. W. Dijkstra in 1979, he was told that, if he wanted to do computer science, he had better learn some math. His favorite xkcd is 612.

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Amazon.com: 4.6 von 5 Sternen  21 Rezensionen
36 von 37 Kunden fanden die folgende Rezension hilfreich
3.0 von 5 Sternen Many typos in first version 20. September 2013
Von Philly Filly - Veröffentlicht auf Amazon.com
Format:Taschenbuch|Verifizierter Kauf
I found this book invaluable while taking the author's course Coding the Matrix on coursera.org. However, this first version was rushed to press with insufficient editing. It is rife with typos, some of which could mislead readers not already familiar with linear algebra. The index is both skimpy and inaccurate. Unless you currently need this book for a course, I recommend waiting for a corrected edition.
15 von 16 Kunden fanden die folgende Rezension hilfreich
4.0 von 5 Sternen An interesting approach to linear algebra 2. September 2013
Von Arko - Veröffentlicht auf Amazon.com
Format:Taschenbuch|Verifizierter Kauf
The book presents a fresh look at linear algebra. You'd learn things by coding in Python. The way vectors and matrices are treated are quite interesting and different from how they are treated in standard linear algebra libraries. The book guides you to develop a whole linear algebra library from scratch and learn things along the way.
You need to be reasonably comfortable coding in Python to fully appreciate the approach presented in this book. The book has a hands on approach and you need to do the coding exercises to fully appreciate the material presented. If you're not comfortable with Python or don't really want to do coding this book may not be for you. However, if you like programming in Python this is an excellent book to learn/review linear algebra.
The reason I'm giving it 4 stars is because the book contains a huge amount of typos. If you're somewhat comfortable with the subject you'd be able to figure them out but they are a constant annoyance nonetheless.
38 von 47 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen A unique approach to linear algebra! 2. August 2013
Von Rafael Espericueta - Veröffentlicht auf Amazon.com
Format:Taschenbuch|Verifizierter Kauf
I'm one of the thousands of students who signed up for Coursera's "Coding the Matrix" class taught by Philip Klein, the author himself. I highly recommend this book to anyone with the necessary prerequisites. You need to be a competent programmer (and preferably in Python), and you need all the usual prerequisites for a linear algebra class. Linear algebra requires some mathematical maturity; where I teach (Bakersfield College) we require three semesters of calculus as prerequisite. It's not that calculus is needed (but for an occasional example), but that it usually signifies the appropriate level of mathematical maturity. For anyone with the prerequisites, this book is going to be quite a lot of fun, and will explore some very interesting applications of linear algebra. The book guides you in the coding up your own linear algebra library using Python 3 (& without numpy), as it explores linear algebra. If you purchase this text, be on the lookout for a future offering of the Coursera course. Combined with that free course, this text becomes far more than just a book.
24 von 30 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen The text makes a hard course less hard and also more interesting. 7. August 2013
Von Bloomington Student - Veröffentlicht auf Amazon.com
Format:Taschenbuch
This review addresses a few questions which students taking or thinking of taking Prof. Klein's Coursera Course (with the same title as the text) might have.

1. Does the text provide the necessary background in python? The text builds on the fundamentals of python programming which one usually gets in introductory courses. Chapter 0 provides an excellent introduction to the python comprehension procedures that are employed throughout the course.

2. Does the text provide the background material required to tackle the weekly assignments? Each chapter explains from a computational perspective the linear algebra concepts and definitions employed in the homework. It also discusses interesting applications that are just mentioned in passing during the lectures.

3. Is the text really necessary to complete the course successfully? Strictly speaking the lectures are sufficient. The text reveals how generously prepared the slides and the homework are. But using the text is much more efficient. It can easily save the students hours and hours of trial and error.
16 von 20 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Without the concrete coding examples I still wouldn't get this topic my 2nd time around. 10. August 2013
Von KM - Veröffentlicht auf Amazon.com
Format:Taschenbuch|Verifizierter Kauf
Yes I'm one of the hoard who are currently taking this course online at coursera.org. I have felt like giving up many times but the book with the working labs eventually brings me back.
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