Lectures related to Accelerated Algorithms for Monotone Inclusions

Newton's Method: Optimization Techniques

Explores optimization techniques like gradient descent, line search, and Newton's method for efficient problem-solving.

Covers the trust-region method and introduces the truncated conjugate gradients method.

Optimization Methods in Machine Learning

Explores optimization methods in machine learning, emphasizing gradients, costs, and computational efforts for efficient model training.

Solving Equations: Newton's Method

Covers Newton's method conditions and convergence, extension to multiple variables, and linear models.

Convex Optimization: Gradient Algorithms

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

Conjugate Gradient Method: Iterative Optimization

Covers the conjugate gradient method, stopping criteria, and convergence properties in iterative optimization.

Optimization Techniques: Stochastic Gradient Descent and Beyond

Discusses optimization techniques in machine learning, focusing on stochastic gradient descent and its applications in constrained and non-convex problems.