Aléatoire : une introduction aux probabilités - Partie 2 (Coursera)

Offered by École Polytechnique,
Aléatoire : une introduction aux probabilités - Partie 2 (Coursera)

Ce cours d'introduction aux probabilités a la même contenu que le cours de tronc commun de première année de l'École polytechnique donné par Sylvie Méléard. Le cours introduit graduellement la notion de variable aléatoire et culmine avec la loi des grands nombres et le théorème de la limite centrale.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

Les notions mathématiques nécessaires sont introduites au fil du cours et de nombreux exercices corrigés sont proposés.
Ce cours propose aussi une introduction aux méthodes de simulations des variables aléatoires comme la méthode de Monte Carlo. Des expériences numériques interactives sont également mises à votre disposition pour vous permettre de visualiser diverses notions.

Syllabus

WEEK 1
Vecteurs aléatoires (1/2)
Nous entamons cette semaine le Cours 4 dont le sujet est les vecteurs aléatoires, c'est-à-dire, une collection finie de variables aléatoires réelles, comme par exemple des couples de variables aléatoires. Ce cours s'étend sur deux semaines.

WEEK 2
Vecteurs aléatoires (2/2)
Il s'agit de la suite et de la fin du Cours 4. Nous allons en particulier généraliser le résultat qui nous permet de faire des calculs de lois.

WEEK 3
Convergences et loi des grands nombres (1/2)
Nous entamons le Cours 5 dont l'objet principal est le théorème communément appelé la « loi des grands nombres ». Nous introduirons aussi plusieurs notions de convergence d'une suite de variables aléatoires.

WEEK 4
Convergences et loi des grands nombres (2/2)
Nous terminons le Cours 5 en donnant des exemples d'applications de la loi des grands nombres. Nous introduisons également la méthode de Monte Carlo.

WEEK 5
Fonctions caractéristiques, convergence en loi et théorème de la limite centrale (1/2)
Nous commençons le Cours 6, le dernier de ce MOOC, à cheval sur deux semaines. Cette semaine, on introduit un nouvel outil très puissant : les fonction caractéristiques.

WEEK 6
Fonctions caractéristiques, convergence en loi et théorème de la limite centrale (2/2)
Cette dernière semaine est consacrée au second pilier de la théorie des probabilités : le théorème de la limite centrale. Ce résultat nécessite une nouvelle notion de convergence : la convergence en loi. Nous verrons notamment une application aux intervalles de confiance qui sont utilisés pour les sondages.

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Matrix Methods (Coursera) Coursera
University of Minnesota

Matrix Methods (Coursera)

Mathematical Matrix Methods lie at the root of most methods of machine learning and data analysis of tabular data. Learn the basics of Matrix Methods, including matrix-matrix multiplication, solving linear equations, orthogonality, and best least squares approximation. Discover the Singular Value Decomposition that plays a fundamental role in dimensionality reduction, Principal Component Analysis, and noise reduction.

Oct 12th 2026
5-12 Weeks
Supply Chain Optimization (Coursera) Coursera
University of California, Irvine

Supply Chain Optimization (Coursera)

Optimization is an important piece of an agile supply chain. In this course, we will explore the components of optimization and how to set up an optimization problem in Excel. We will also practice capacity and resource optimization and explore examples of both in the supply chain. Building off of our optimization practice, we will next learn how to use a Monte Carlo simulation to make the least risky decision in uncertain supply chain situations.

Sep 21st 2026
4 Weeks
Mathematical Methods for Quantitative Finance (edX) EdX
MIT,MITx

Mathematical Methods for Quantitative Finance (edX)

Learn the mathematical foundations essential for financial engineering and quantitative finance: linear algebra, optimization, probability, stochastic processes, statistics, and applied computational techniques in R. Modern finance is the science of decision making in an uncertain world, and its language is mathematics. As part of the MicroMasters® Program in Finance, this course develops the tools needed to describe financial markets, make predictions in the face of uncertainty, and find optimal solutions to business and investment decisions.

Jun 26th 2024
5-12 Weeks
Introduction to Computational Thinking and Data Science (edX) EdX
MIT,MITx

Introduction to Computational Thinking and Data Science (edX)

This course is an introduction to using computation to understand real-world phenomena. This course will teach you how to use computation to accomplish a variety of goals and provides you with a brief introduction to a variety of topics in computational problem solving. This course is aimed at students with some prior programming experience in Python and a rudimentary knowledge of computational complexity.

Mar 20th 2024
5-12 Weeks
Managing Uncertainty in Marketing Analytics (Coursera) Coursera
Emory University

Managing Uncertainty in Marketing Analytics (Coursera)

Marketers must make the best decisions based on the information presented to them. Rarely will they have all the information necessary to predict what consumers will do with complete certainty. By incorporating uncertainty into the decisions that they make, they can anticipate a wide range of possible outcomes and recognize the extent of uncertainty on the decisions that they make. In Incorporating Uncertainty into Marketing Decisions, learners will become familiar with different methods to recognize sources of uncertainty that may affect the marketing decisions they ultimately make.

Sep 21st 2026
4 Weeks
Enseñanza de las matemáticas de primaria (Coursera) Coursera
Universidad de los Andes

Enseñanza de las matemáticas de primaria (Coursera)

En este tercer curso de acceso gratuito* del programa especializado Educación Matemática para profesores de primaria, conocerás los conceptos y técnicas para planificar e implementar tus clases. El curso tiene una duración aproximada de seis semanas, con una dedicación promedio de 4 horas semanales. Todas las evaluaciones tienen retroalimentación y podrás descargar la mayoría de los recursos del curso.

Oct 12th 2026
5-12 Weeks
Statistical Mechanics: Algorithms and Computations (Coursera) Coursera
École normale supérieure

Statistical Mechanics: Algorithms and Computations (Coursera)

In this course you will learn a whole lot of modern physics (classical and quantum) from basic computer programs that you will download, generalize, or write from scratch, discuss, and then hand in. Join in if you are curious (but not necessarily knowledgeable) about algorithms, and about the deep insights into science that you can obtain by the algorithmic approach.

Sep 28th 2026
5-12 Weeks
Introduction to Complexity Science (Coursera) Coursera
National University of Singapore

Introduction to Complexity Science (Coursera)

This course explores the features of complexity science. Our world is connected by an abundance of complex systems. Across all levels of organizations from physical, biological world to the social world, we may think of the connectivity between individual elements and how they interact and influence each other. For example, how humans transmit pandemics within a group, how cars interact in the traffic system and how networks connect in governmental organizations. Although these systems are diverse and different, they have surprisingly huge features in common.

Oct 5th 2026
5-12 Weeks