Optimization Basics: Norms, Convexity, Differentiability

This lecture covers the fundamentals of optimization, including norms, convexity, differentiability, and smoothness. It delves into topics such as the Jacobian matrix, chain rule via Jacobians, quadratic and logistic loss functions, convex sets, convex hulls, convexity of functions, subdifferentials, Lipschitz gradient functions, logistic regression, strong convexity, and convergence rates of sequences.