Sparsity and Compression
Offered By: Steve Brunton via YouTube
Course Description
Overview
Syllabus
Why images are compressible: The Vastness of Image Space.
What is Sparsity?.
Compressed Sensing: Overview.
Compressed Sensing: Mathematical Formulation.
Underdetermined systems and compressed sensing [Python].
Underdetermined systems and compressed sensing [Matlab].
Beating Nyquist with Compressed Sensing.
Shannon Nyquist Sampling Theorem.
Beating Nyquist with Compressed Sensing, part 2.
Beating Nyquist with Compressed Sensing, in Python.
Sparsity and the L1 Norm.
Compressed Sensing: When It Works.
Robust Regression with the L1 Norm.
Robust Regression with the L1 Norm [Matlab].
Robust Regression with the L1 Norm [Python].
Robust, Interpretable Statistical Models: Sparse Regression with the LASSO.
Sparse Representation (for classification) with examples!.
Robust Principal Component Analysis (RPCA).
Robust Modal Decompositions for Fluid Flows.
Sparse Sensor Placement Optimization for Reconstruction.
Sparse Sensor Placement Optimization for Classification.
Sparsity and Parsimonious Models: Everything should be made as simple as possible, but no simpler.
PySINDy: A Python Library for Model Discovery.
Taught by
Steve Brunton
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