EdX

Algèbre Linéaire (Partie 1) (edX)

Algèbre Linéaire (Partie 1) (edX)

Un MOOC francophone d'algèbre linéaire accessible à tous, enseigné de manière rigoureuse et ne nécessitant aucun prérequis. Vous voulez apprendre l'algèbre linéaire, un précieux outil complémentaire à vos connaissances acquises durant vos études en économie, ingénierie, physique, ou statistique? Ou simplement pour la beauté de la matière? Alors ce cours est fait pour vous!

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

Outre remplir le rôle d'outil dans les différentes branches mentionnées ci-dessus (permettant la résolution de problèmes concrets), l'algèbre linéaire, qui capture l'essence des mathématiques -à savoir, l'algèbre et la géométrie- vous introduira au monde plus abstrait des mathématiques.
Proposé comme complément de cours aux ingénieurs de première année à l'Ecole Polytechnique Fédérale de Lausanne, ce MOOC (composé de trois parties) n'en est pas moins un cours à part entière et peut être considéré comme une base solide d'algèbre linéaire pour tout étudiant intéressé par l'apprentissage de cette matière.

Bien que les vidéos constituent le coeur du cours, des exercices de type QCM (Questions à choix multiples) ainsi que des séries au format PDF seront disponibles chaque semaine, ainsi que des corrigés appropriés. Plus précisément, les séries d'exercices seront accompagnées d'un corrigé au format PDF et certains problèmes bénéficieront d'une correction détaillée en vidéo, dans laquelle l'un des enseignants présentera la solution, étape par étape. Finalement, chaque vidéo de cours sera suivie d'un quiz, dont le but est de tester le degré d’assimilation des connaissances acquises.
Le cours est organisé en dix chapitres dans lesquels une approche très détaillée des concepts théoriques est proposée, ainsi que de multiples exemples illustratifs :
1) Systèmes d'équations linéaires.
2) Algèbre matricielle.
3) Espaces vectoriels.
4) Bases et dimensions.
5) Applications linéaires.
6) Matrices et applications linéaires.
7) Déterminants.
8) Vecteurs propres, valeurs propres, diagonalisation.
9) Produits scalaires et espaces euclidiens.
10) Matrices orthogonales et matrices symétriques.
Cette première partie du cours sera dévouée à l'étude des quatre premiers chapitres cités plus haut. Aucune connaissance particulière n’est requise pour comprendre les concepts abordés dans ce MOOC, mais il est conseillé de travailler régulièrement et de manière assidue, de façon à ne pas prendre de retard lors de l'apprentissage de la matière.

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

Related Courses

Introduction to Quantum Science & Technology (edX) EdX
Purdue University,PurdueX

Introduction to Quantum Science & Technology (edX)

Learn about fundamental concepts and engineering challenges of quantum technologies. Emerging quantum systems are disruptive technologies redefining computing and communication. Teaching quantum physics to engineers and educating scientists on engineering solutions are critical to address fundamental and engineering challenges of the quantum technologies.

Aug 21st 2023
13-24 Weeks
Mathematics for Machine Learning: Linear Algebra (Coursera) Coursera
Imperial College London

Mathematics for Machine Learning: Linear Algebra (Coursera)

In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works.

Jul 27th 2026
5-12 Weeks
Foundations of Data Science (edX) EdX
Indian Institute of Management, Bangalore,IIMBx

Foundations of Data Science (edX)

Learn the fundamental concepts in probability, statistics, optimization and linear algebra which form the foundations for data science. Data Science along with artificial intelligence (AI) and its various components such as statistical learning (SL), machine learning (ML) and deep learning algorithms (DL) are recognized as main drivers of organizational value creation. According to Dr Jim Gray, Data Science is the fourth paradigm which drives innovative solutions to organizational problems.

This course is archived
5-12 Weeks
Linear Algebra I: Vectors and Linear Equations (edX) EdX
Delft University of Technology,DelftX

Linear Algebra I: Vectors and Linear Equations (edX)

This course provides an overview of bachelor-level linear algebra. You will review all the concepts and practice and refresh the skills related to vectors and linear equations. A strong foundation in mathematics is critical for success in all science and engineering disciplines. Whether you want to make a strong start to a master’s degree, prepare for more advanced courses, solidify your knowledge in a professional context or simply brush up on fundamentals, this course will get you up to speed.

Self Paced
Self-Paced
Introduction to Linear Models and Matrix Algebra (edX) EdX
HarvardX,Harvard University

Introduction to Linear Models and Matrix Algebra (edX)

Learn to use R programming to apply linear models to analyze data in life sciences. Matrix Algebra underlies many of the current tools for experimental design and the analysis of high-dimensional data. In this introductory data analysis course, we will use matrix algebra to represent the linear models that commonly used to model differences between experimental units. We perform statistical inference on these differences. Throughout the course we will use the R programming language.

Self Paced
Self-Paced
Introduction to Linear Algebra (Coursera) Coursera
The University of Sydney

Introduction to Linear Algebra (Coursera)

Linear algebra and calculus are the two most important foundational pillars on which modern mathematics is built. They are studied by almost all mathematics students at university, though typically labelled as different subjects and taught in parallel. Over time, students discover that linear algebra and calculus are inseparable (but not identical) twins that interlock to form the backbone of almost all applications of mathematics to physical and biological sciences, engineering and computer science. It is recommended that participants in the MOOC Introduction to Linear Algebra have already taken, or take in parallel, the MOOC Introduction to Calculus.

Aug 3rd 2026
4 Weeks
Advanced Linear Models for Data Science 1: Least Squares (Coursera) Coursera
Johns Hopkins University

Advanced Linear Models for Data Science 1: Least Squares (Coursera)

Welcome to the Advanced Linear Models for Data Science Class 1: Least Squares. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following: a basic understanding of linear algebra and multivariate calculus; a basic understanding of statistics and regression models; at least a little familiarity with proof based mathematics; basic knowledge of the R programming language.

Jul 27th 2026
5-12 Weeks
Linear Algebra IV: Orthogonality & Symmetric Matrices and the SVD (edX) EdX
Georgia Institute of Technology,GTx

Linear Algebra IV: Orthogonality & Symmetric Matrices and the SVD (edX)

This course takes you through roughly five weeks of MATH 1554, Linear Algebra, as taught in the School of Mathematics at The Georgia Institute of Technology. In the first part of this course you will explore methods to compute an approximate solution to an inconsistent system of equations that have no solutions. Our overall approach is to center our algorithms on the concept of distance.

Self Paced
Self-Paced