Lectures related to Compressed sensing

Delves into L1 regularization, sparse solutions, and dimensionality reduction in the context of machine learning.

Explores concise signal models, compressive sensing, sparsity, atomic norms, and non-smooth minimization using subgradient descent.

Explores LASSO regression for inducing sparsity in signals through gradient descent.

Explores non-smooth minimization, compressive sensing, sparse signal recovery, and simple representations using atomic sets and atoms.