From Stochastic Gradient Descent to Non-Smooth Optimization

This lecture covers topics such as stochastic optimization, deficiency of smooth models, sparsity, compressive sensing, and non-smooth minimization via subgradient descent. The instructor explains the concept of atomic norms and the application of stochastic gradient descent in statistical learning with streaming data.