Lectures related to Convexity and Jacobians

Covers the generalities of functions, including the definition of an application between sets and the uniqueness of elements in the image set.

Covers the properties and operations of convex functions.

Covers the basic concepts of sets, functions, and number systems.

Explores optimization basics such as norms, convexity, and differentiability, along with practical applications and convergence rates.

Covers the definition of applications, image of elements, and direct and reciprocal images.

Introduces sets in discrete mathematics and explores proof techniques like direct and indirect proofs.

Explores solutions of differential equations and properties of functions.

Covers organizational aspects, introduces Kahut for quizzes, presents complex examples, and summarizes the main topics.

Covers the basics of tuples, lists, sets, linear types, conversions, dictionaries, and defaultdicts.

Introduces the purpose of mastering mathematics and calculation tools for engineers, emphasizing the need to think methodically and rigorously.

Explores the properties of relations in computer science, including equivalence relations and the partition of a set.

Introduces sets, functions, Cartesian products, and compositions, discussing images, preimages, and function properties.

Explores recursively defined sets, natural numbers, strings, functions, string concatenation, and well-formed formulae.

Introduces logic, proofs, sets, functions, and algorithms in mathematics and computer science.

Covers applications between sets, functions, and graphs, exploring injectivity, surjectivity, and bijectivity.

Covers linear algebra concepts with a focus on functions and their properties.

Covers the basics of discrete mathematics, focusing on logic, structures, and algorithms for computer systems.

Covers advanced Python programming concepts, including data structures and functions.

Explores fixed points in G-sets, applications between sets, and the construction of orbits.

Explores VC dimension, gradient descent, convex sets, and Lipschitz functions in convex optimization.