{
    "video":"IWugXcWpfoM",
    "links":{},
    "estimatedClassWorkload":"7-10 hours/week",
    "instructor":"Phil Klein",
    "largeIcon":"https://d15cw65ipctsrr.cloudfront.net/6c/ffdfc097878bc0f8115148d7ee6826/460_259_logo.jpg",
    "id":198,
    "smallIconHover":"https://d15cw65ipctsrr.cloudfront.net/c9/8b3a8357fe29791971ece8cc8fb35b/460_259_logo.jpg",
    "universityLogo":"",
    "smallIcon":"https://d15cw65ipctsrr.cloudfront.net/c9/8b3a8357fe29791971ece8cc8fb35b/460_259_logo.jpg",
    "faq":"<ul><li><b>Will I get a statement of accomplishment&nbsp;after completing this class?<br></b><br>Yes. Students who successfully complete the class will receive a statement of accomplishment&nbsp;signed by the instructor.<br><br></li><li><b>What resources will I need for this class?</b><br><br>You will need a computer with Python installed (version 3.x). We will provide additional Python modules for you to download.<br><br></li><li><b>What is the coolest thing I'll learn if I take this class?</b><br><br>Here are some cool things: removing the perspective from an image, &nbsp;a simple machine-learning algorithm applied to cancer data.</li></ul>",
    "universityLogoSt":"",
    "courseSyllabus":"<ul><li>The Function</li><li>The Field</li><li>The Vector</li><li>The Vector Space</li><li>The Matrix</li><li>The Basis</li><li>Dimension</li><li>Gaussian Elimination</li><li>The Inner Product</li><li>Orthogonalization</li></ul>",
    "shortName":"matrix",
    "targetAudience":0,
    "shortDescription":"Learn the concepts and methods of linear algebra, and how to use them to think about computational problems arising in computer science.  Coursework includes building on the concepts to write small programs and run them on real data. ",
    "aboutTheCourse":"<div>When you take a digital photo with your phone or transform the image in Photoshop, when you play a video game or watch a movie with digital effects, when you do a web search or make a phone call, you are using technologies that build upon linear algebra. &nbsp;Linear algebra provides concepts that are crucial to many areas of computer science, including graphics, image processing, cryptography, machine learning, computer vision, optimization, graph algorithms, quantum computation, computational biology, information retrieval and web search. Linear algebra in turn is built on two basic elements, the matrix and the vector. &nbsp;</div><div><br></div><div>In this class, you will learn the concepts and methods of &nbsp;linear algebra, and&nbsp;how to use them&nbsp;to think about problems arising in computer science. &nbsp;You will write small programs in the programming language Python to &nbsp;implement basic matrix and vector functionality and algorithms, and use these to process real-world data to achieve such tasks as: two-dimensional graphics transformations, face morphing, face detection, image transformations such as blurring and edge detection, image perspective removal, classification of tumors as malignant or &nbsp;benign, integer factorization, error-correcting codes, and secret-sharing.</div><div><br></div>",
    "videoId":"IWugXcWpfoM",
    "courseFormat":"",
    "aboutTheInstructor":"<div><div><img class=\"coursera-instructor-thumb\" src=\"https://coursera-topic-photos.s3.amazonaws.com/db/2b950a6da9c516b9b88cac1e15080b/klein_photo.jpg\"><br>Philip Klein is Professor of Computer Science at Brown University. \u00a0He was a recipient of the National Science Foundation's Presidential Young Investigator Award, and has received multiple research grants from the National Science Foundation. \u00a0He 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.\u00a0</div><div><br></div><div>Klein received a B.A. in Applied Mathematics from Harvard and a Ph.D. in Computer Science from MIT. \u00a0He 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.\u00a0</div><div><br></div><div>Klein has worked at industry research labs, including Xerox PARC and AT&T Labs. \u00a0He has been Chief Scientist at three successful start-ups.\u00a0</div></div>",
    "recommendedBackground":"You should be an experienced programmer. &nbsp;We use a subset of Python in this course, and we start by covering the relevant features and syntax, so many students find they can get by without prior knowledge of Python.<br><br>You are&nbsp;<i>not</i>&nbsp;expected to have any background in linear algebra. &nbsp;However, you should be prepared to read and understand some mathematical proofs.&nbsp;At Brown University, a similar course is taken mostly by sophomore computer science &nbsp;majors who have taken at least two semesters of programming and one semester addressing proof techniques.<br>",
    "name":"Coding the Matrix: Linear Algebra through Computer Science Applications",
    "suggestedReadings":"<a href=\"http://www.amazon.com/dp/0615880991/\" target=\"_blank\">Coding the Matrix</a> is an optional companion textbook. \u00a0It covers the material addressed by this course, plus additional examples and more advanced topics not covered by the course (wavelets, discrete Fourier transforms, singular value decomposition, eigenvalues, and linear programming). \u00a0The textbook is not at all necessary for taking the course; all necessary material is covered in lecture. \u00a0",
    "photo":"https://coursera-course-photos.s3.amazonaws.com/ec/6faacf42b19cf2772061ae095d98ee/460_259_logo.jpg"
}