Lectures related to Optimization Methods: Convexity and Gradient Descent

Explores the optimality of convergence rates in convex optimization, focusing on accelerated gradient descent and adaptive methods.

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

Covers the probabilistic model for linear regression and the importance of regularization techniques.