The course has been taught at Brown University since 2008,
and is being taught in Fall
2013. Slides will be made available here.
A shortened version has been taught through Coursera. The aim of this course is to provide students interested in computer science an introduction to vectors and matrices and their use in CS applications.
The course is driven by applications from areas chosen from among: computer vision, cryptography, game theory, graphics, information retrieval and web search, and machine learning.
Data and support code required for carrying out the assignments are provided here.
Auto-grading is made available for some of the tasks.
Here are the first and second labs from Edition One. These have nothing to do with linear algebra. They are provided to bring the reader up to speed in the part of Python we use in the book. Here is a document intended to assist people with making the transition from loops to comprehensions. You can use Python Tutor to debug, visualize, and check your solutions to the tasks in this lab.Join the mailing list for updates about addition of resources.
1 of the textbook
is available for purchase.
It incorporates corrections and
a revised and expanded index.
List Price: $35.00
Publisher: Newtonian Press
Purchase in UK
Purchase in Germany
Errata for Edition 1
Here is a revised
and expanded index
if you have questions about
Here are examples of applications addressed in Coding the
Error-correcting codes are used, e.g., by cellphones to preserve data transmitted over a noisy channel while maintaining high throughput.
522253825433285668885771662040104167 = 891428822186035241∙585861498344390287
Factoring an integer is a hard computational problem (and the RSA cryptosystem depends on it being hard). At the core of the most sophisticated integer-factoring algorithms is a simple problem in linear algebra.
Searching within an audio clip
Searching within an image
Convolution can also be done in two dimensions, enabling one to quickly search for a subimage within an image.
Audio and image compression
Compression of audio and images aids efficient storage and transmission. Lossy compression techniques such as those used in MP3 (audio) and JPEG (images) are based in part on linear algebra,
e.g. wavelet transform and Fourier transform.
100% original size
40% original size
10% original size
2d graphics transformations
Minimum-weight spanning forest