Sparse Representations in Signal and Image Processing: Fundamentals

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Sparse Representations in Signal and Image Processing: Fundamentals

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FREE,
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edX online courses
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Harvard University, the Massachusetts Institute of Technology, and the University of California, Berkeley, are just some of the schools that you have at your fingertips with edX. Through massive open online courses (MOOCs) from the world's best universities, you can develop your knowledge in literature, math, history, food and nutrition, and more. These online classes are taught by highly-regarded experts in the field. If you take a class on computer science through Harvard, you may be taught by David J. Malan, a senior lecturer on computer science at Harvard University for the School of Engineering and Applied Sciences. But there's not just one professor - you have access to the entire teaching staff, allowing you to receive feedback on assignments straight from the experts. Pursue a Verified Certificate to document your achievements and use your coursework for job and school applications, promotions, and more. EdX also works with top universities to conduct research, allowing them to learn more about learning. Using their findings, edX is able to provide students with the best and most effective courses, constantly enhancing the student experience.

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Course Description

This course introduces the fundamentals of the field of sparse representations, starting with its theoretical concepts, and systematically presenting its key achievements. We will touch on theory and numerical algorithms.

Modeling data is the way we – scientists – believe that information should be explained and handled. Indeed, models play a central role in practically every task in signal and image processing. Sparse representation theory puts forward an emerging, highly effective, and universal such model. Its core idea is the description of the data as a linear combination of few building blocks – atoms – taken from a pre-defined dictionary of such fundamental elements.

A series of theoretical problems arise in deploying this seemingly simple model to data sources, leading to fascinating new results in linear algebra, approximation theory, optimization, and machine learning. In this course you will learn of these achieveme...

This course introduces the fundamentals of the field of sparse representations, starting with its theoretical concepts, and systematically presenting its key achievements. We will touch on theory and numerical algorithms.

Modeling data is the way we – scientists – believe that information should be explained and handled. Indeed, models play a central role in practically every task in signal and image processing. Sparse representation theory puts forward an emerging, highly effective, and universal such model. Its core idea is the description of the data as a linear combination of few building blocks – atoms – taken from a pre-defined dictionary of such fundamental elements.

A series of theoretical problems arise in deploying this seemingly simple model to data sources, leading to fascinating new results in linear algebra, approximation theory, optimization, and machine learning. In this course you will learn of these achievements, which serve as the foundations for a revolution that took place in signal and image processing in recent years.

Reviews 9/10 stars
3 Reviews for Sparse Representations in Signal and Image Processing: Fundamentals

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10/10 starsCompleted
1 year, 3 months ago
A well-designed course. The structure and content are superb, and the material prepared (slides, video, projects) is of top quality.
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10/10 starsCompleted
1 year, 3 months ago
As a machine learning researcher I can tell that I learned new tools that have already influenced a project that I am running. Can’t wait for the second one.
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10/10 starsCompleted
1 year, 3 months ago
excellent course, excellent course, excellent course, excellent course, excellent course, excellent course, excellent course, excellent course, excellent course, excellent course, excellent course, excellent course, excellent course, excellent course, excellent course, excellent course, excellent course, excellent course, excellent course, excellent course, excellent course
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