LASSO Regression: Sparse Signal Induction

This lecture covers the concept of LASSO regression, focusing on the induction of sparsity in signals through the minimization of the LASSO loss function. The instructor explains the assumptions on noise, the process of sparse regression, and the significance of variable selection in compressed sensing. The lecture delves into the gradient descent method used for minimizing the LASSO loss, emphasizing the iterative nature of the process and the criteria for convergence.