Lectures related to Vector Differentiation and Convex Functions

Covers the Gradient Descent method, smoothness in functions, and proofs related to convexity.

Introduces Convex Optimization notation, convex functions, vector norms, and matrix properties.

Covers basics on optimization, including norms, Lipschitz continuity, and convexity concepts.

Covers faster gradient descent methods and projected gradient descent for constrained optimization in machine learning.

Introduces mechanics, differential and vector calculus, and historical perspectives from Aristotle to Newton.

Explores convex optimization theory, covering local and global minima, convex functions, and applications in various fields.