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Lecture 1: Overview and Theory

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ASC - Approximation, sampling and compression in data science

Lecture 1: Overview and Theory  In these lectures I will present an introduction to compressed sensing and sparse approximation.  The first lecture gives an overview of compressed sensing and its standard theory.  Next, I will focus on two major areas of application.  The second lecture considers image reconstruction, and its application to medical and scientific imaging.  The third lecture considers high-dimensional approximation via compressed sensing, with application to parametric PDEs in Uncertainty Quantification.




This talk is part of the Isaac Newton Institute Seminar Series series.

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