Lectures related to Optimization Techniques: Convexity in Machine Learning

Covers the KKT conditions and convex optimization, discussing constraint qualifications and tangent cones of convex sets.

Covers the optimality of convergence rates in accelerated and stochastic gradient descent methods for non-convex optimization problems.

Explores VC dimension, gradient descent, convex sets, and Lipschitz functions in convex optimization.