Lectures related to Newton's Method: Optimization Techniques

Optimization Techniques: Convexity and Algorithms in Machine Learning

Covers optimization techniques in machine learning, focusing on convexity, algorithms, and their applications in ensuring efficient convergence to global minima.

Proximal Gradient Descent: Optimization Techniques in Machine Learning

Discusses proximal gradient descent and its applications in optimizing machine learning algorithms.

Trust region methods: framework & algorithms

Covers trust region methods, focusing on the framework and algorithms.

Optimization Techniques: Convexity in Machine Learning

Covers optimization techniques in machine learning, focusing on convexity and its implications for efficient problem-solving.

Adaptive Gradient Methods

Explores adaptive gradient methods like AdaGrad, AcceleGrad, and UniXGrad, focusing on their local adaptation and convergence rates.

Iterative Descent Methods: Optimization Principles

Explores iterative descent methods in optimization, gradient descent, local minima, and convergence principles.