The actual MOOC materials are not available until you register for the MOOC. The course outline suggests that the emphasis is on solving systems of equations, but does also emphasize vector spaces, dimension, basis, orthogonality, and inner-product spaces. The reduced mathematical content is assumed to have been done to make room in a typical course for the Python programming. Here's more from the Coursera website for this book: 'In this class, you will learn the concepts and methods of linear algebra, and how to use them to think about problems arising in computer science.
About The Course The course has been taught at Brown University since 2008, and is being taught. Slides from past editions of the Brown University course are available. A shortened version has been taught through.
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. Course Resources Data and support code required for carrying out the assignments are provided.
Is made available for some of the tasks. 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. Is a document intended to assist people with making the transition from loops to comprehensions. Join the mailing list for updates about addition of resources.
Slides Slides from the course taught at Brown University in Fall 2013:. Special Bases. The Linear Program Sign up for updates. To receive messages when new material is available, e.g. Blog posts about applications of linear algebra to CS, news of a follow-on course, or corrections to the book, join the mailing list. I promise that mailings will be rare and that I will not share your email address with anybody, ever.
Email Address: Subscribe Unsubscribe About The Book. of the textbook is available for. It incorporates corrections and a revised and expanded index. List Price: $35.00 Publisher: Here is a for Edition 0. Send if you have questions about using the book for a university course.
Example Applications Here are examples of applications addressed in Coding the Matrix. crossfade A line segment between points is given by the convex combinations of those points; if the 'points' are images, the line segment is a simple morph between the images. Perspective rectification Given a photo of a whiteboard taken at an angle, synthesize a perspective-free view of the whiteboard. The same transformation can be used in using a Wiimote to make a low-cost interactive whiteboard or light pen (due to Johnny Chung Lee). Error-correcting codes Error-correcting codes are used, e.g., by cellphones to preserve data transmitted over a noisy channel while maintaining high throughput. Integer factorization 104167 = 8600287 Factoring an integer is a hard computational problem (and the RSA cryptosystem depends on it being hard).
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At the core of the most sophisticated integer-factoring algorithms is a simple problem in linear algebra. Image blurring Blurring an image is a simple linear transformation. Searching within an audio clip Searching for one audio clip within another can be formulated as a convolution. A convolution can be computed very quickly using the Fast Fourier Transform. 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. Face detection A 'classical' approach to face detection is eigenfaces, a technique related to principal component analysis. 2d graphics transformations Simple transformations that arise in graphics such as rotation, translation, and scaling can be expressed using matrices. Lights Out Lights Out is a puzzle in which you must select the correct buttons to push in order for all the lights to go out.
Finding a solution can be expressed as a problem in linear algebra. Minimum-weight spanning forest Finding the minimum-weight spanning tree of a graph can be interpreted as the problem of finding a minimum-weight basis for a vector space derived from the graph. Graph layout A nice drawing of a graph can be obtained from eigenvectors of a related matrix.
Linear Algebra is a my first mathematical love. I never get tired of it for some reason. As such, I've collected quite a few LA texts, and I now have a new favorite! Here are some of the reasons I really enjoy this text: Coding the Matrix is a computational approach to LA and starts with the assumption of a finite domain, thus, it is able to focus on a lot of practical computational examples, and skip the complications of infinite domains. Adding to the above notion, the book focuses on portraying vectors as functions on finite domains which is a wonderful way to present them as it makes connecting them to practical applications very direct.
Everything is in Python. I don't make this claim because I'm a Python fan (I am, but not a daily user or fanboy). I point this out because Python is used in ML and compsci, and therefore will be at least familiar to many readers. In addition, even those who love other languages would agree that Python has great mathematical tools, a large community, is free, is great for 'sketching' things quickly with tools like IPython notebooks.
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In addition, and this isn't Python-specific, the author has us think of vectors as functions on finite domains, and as such, there is heavy use of representing vectors as sparse dictionaries, which languages like Python do quite well. The text spends a lot of time on signals, sparsity, compression, and other popular compsci topics.
edit for this subreddit: there are a ton of examples which ML enthusiasts will recognize throughout the book. The author seems to have an endless supply of simple, practical examples which are sometimes just sprinkled in the main text, or presented as labs or asides. There are a lot of great popular culture references to The Matrix, other Movies, and tons of xkcd comics which are always very related to the topic in the text, but keep the volume from being oppressive. Anyway, if you like linear algebra topics, and lots of great compsci applications thereof, I highly recommend this book.
Enjoy compsciers!
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' A new edition of this text, incorporating corrections and an expanded index, has been issued as of September 4, 2013, and will soon be available on Amazon. 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.
Ad&d 2e character sheet fillable. 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. 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.
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. 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.
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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.
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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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