Lectures related to Taylor Expansion and Convex Functions

Proximal Gradient Descent: Optimization Techniques in Machine Learning

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

Mathematics of Data: Optimization Basics

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

Taylor Polynomials and Function Properties

Covers Taylor polynomials, function properties, double integrals, partial derivatives, and function boundaries.

Optimal Transport: Rockafellar Theorem

Explores the Rockafellar Theorem in optimal transport, focusing on c-cyclical monotonicity and convex functions.

Multivariable Integral Calculus

Covers multivariable integral calculus, including rectangular cuboids, subdivisions, Douboux sums, Fubini's Theorem, and integration over bounded sets.

Convex Functions

Covers the properties and operations of convex functions.

Continuous Functions on Closed Interval

Explores continuous functions on closed intervals, emphasizing the importance of understanding definitions for continuity.

Functions and Derivatives

Explores continuity, differentiability, boundedness, and extrema of functions in multiple variables.