Lectures related to Quasi-Newton Methods

Covers the convergence of Newton's method and its applications in numerical analysis.

Covers the Newton method and its convergence properties near the optimal point.

Discusses proximal and subgradient descent methods for optimization in machine learning.

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

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

Explains Newton's method of order 2 for finding function zeros.