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Robust, Interpretable Statistical Models: Sparse Regression with the LASSO
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Classroom Contents
Sparsity and Compression
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- 1 Why images are compressible: The Vastness of Image Space
- 2 What is Sparsity?
- 3 Compressed Sensing: Overview
- 4 Compressed Sensing: Mathematical Formulation
- 5 Underdetermined systems and compressed sensing [Python]
- 6 Underdetermined systems and compressed sensing [Matlab]
- 7 Beating Nyquist with Compressed Sensing
- 8 Shannon Nyquist Sampling Theorem
- 9 Beating Nyquist with Compressed Sensing, part 2
- 10 Beating Nyquist with Compressed Sensing, in Python
- 11 Sparsity and the L1 Norm
- 12 Compressed Sensing: When It Works
- 13 Robust Regression with the L1 Norm
- 14 Robust Regression with the L1 Norm [Matlab]
- 15 Robust Regression with the L1 Norm [Python]
- 16 Robust, Interpretable Statistical Models: Sparse Regression with the LASSO
- 17 Sparse Representation (for classification) with examples!
- 18 Robust Principal Component Analysis (RPCA)
- 19 Robust Modal Decompositions for Fluid Flows
- 20 Sparse Sensor Placement Optimization for Reconstruction
- 21 Sparse Sensor Placement Optimization for Classification
- 22 Sparsity and Parsimonious Models: Everything should be made as simple as possible, but no simpler
- 23 PySINDy: A Python Library for Model Discovery
