Catégorie
Analyse fonctionnelle (mathématiques)
Cours associés (203)
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The goal of this course is to give an introduction to the theory of distributions and cover the fundamental results of Sobolev spaces including fractional spaces that appear in the interpolation theor
MATH-305: Introduction to partial differential equations
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MATH-302: Functional analysis I
Concepts de base de l'analyse fonctionnelle linéaire: opérateurs bornés, opérateurs compacts, théorie spectrale pour les opérateurs symétriques et compacts, le théorème de Hahn-Banach, les théorèmes d
MATH-115(b): Advanced linear algebra II
L'objectif du cours est d'introduire les notions de base de l'algèbre linéaire et de démontrer rigoureusement les résultats principaux du sujet.
MATH-432: Probability theory
The course is based on Durrett's text book
Probability: Theory and Examples.
It takes the measure theory approach to probability theory, wherein expectations are simply abstract integrals.
It takes the measure theory approach to probability theory, wherein expectations are simply abstract integrals.
MATH-206: Analysis IV
En son coeur, c'est un cours d'analyse fonctionnelle pour les physiciens et traite les bases de théorie de mesure, des espaces des fonctions et opérateurs linéaires.
MATH-404: Functional analysis II
We introduce locally convex vector spaces. As an example we treat the space of test functions and the space of distributions. In the second part of the course, we discuss differential calculus in Bana
MATH-410: Riemann surfaces
This course is an introduction to the theory of Riemann surfaces. Riemann surfaces naturally appear is mathematics in many different ways: as a result of analytic continuation, as quotients of complex
PHYS-739: Conformal Field theory and Gravity
This course is an introduction to holography, the modern approach to quantum gravity.
EE-556: Mathematics of data: from theory to computation
This course provides an overview of key advances in continuous optimization and statistical analysis for machine learning. We review recent learning formulations and models as well as their guarantees