Lectures related to Policy Gradient Methods: Convergence and Optimization

Covers the properties and operations of convex functions.

Introduces optimization basics, covering logistic regression, derivatives, convex functions, gradient descent, and second-order methods.

Covers convex optimization problems and gradient-based algorithms to find the global minimum.

Discusses advanced optimization techniques, focusing on faster and projected gradient descent methods in machine learning.

Covers non-convex optimization techniques and their applications in machine learning.

Explores stochastic optimization algorithms and methods for convex problems with smooth and nonsmooth risks.