Gradient Descent for Linear MSE

This lecture covers the concept of Gradient Descent for Linear Mean Squared Error (MSE) in machine learning. It explains the computation of the gradient, the complexity of computing the gradient, and the variant with an offset term. The lecture also delves into Stochastic Gradient Descent, the use of penalty functions for constrained optimization, and the implementation issues such as adaptive step-size selection and feature normalization. Additionally, it discusses non-convex optimization, stopping criteria, optimality conditions, and the transformation of constrained problems into unconstrained ones.