Les nouvelles technologies de l’information ont facilité l’accès à de nombreuses bases de données offrant au grand public, mais surtout aux professionnels, une multitude de services. Le domaine de l’information géographique a également suivi ce mouvement en modernisant l’ensemble des supports, des plans, des cartes topographiques et de tous les types de données à référence spatiale. Face au déploiement massif des cartes numériques et des nombreux services basés sur la localisation, il s’agit de rester critique et surtout de développer les capacités nécessaires afin de choisir les outils et jeux de géodonnées adaptés aux besoins professionnels. C’est dans cette optique que ce cours propose de développer les éléments fondamentaux de la géomatique en décrivant les domaines clés que sont: les références géodésiques, les techniques d’acquisition des géodonnées, la topométrie, la localisation par satellites et la modélisation et représentation du terrain. Cet enseignement est proposé aux futurs ingénieurs et architectes qui ont recours aux géodonnées pour la réalisation de projets d’aménagement, de construction, de gestion de l’environnement, de transport et de développement territorial. Dans ces domaines, l’accès aux données à référence spatiale ainsi qu’une connaissance des sources d’information et de leur qualité sont donc primordiales pour la conduite de projets.

This first course on concepts of single variable calculus will introduce the notions of limits of a function to define the derivative of a function. In mathematics, the derivative measures the sensitivity to change of the function. For example, the derivative of the position of a moving object with respect to time is the object's velocity: this measures how quickly the position of the object changes when time advances. This fundamental notion will be applied through the modelling and analysis of data.

Le cours expose la théorie de Galois, du classique critère de non-résolubilité des équations polynomiales aux méthodes plus avancées de calcul de groupes de Galois par réduction modulo un nombre premier. Le thème général de cette théorie est l'étude des racines d'un polynôme et concerne en particulier la possibilité de les exprimer à partir des coefficients de ce polynôme. Evariste Galois considère les symétries de ces racines et associe ainsi à ce polynôme un groupe de permutations de ses racines, que l'on appelle maintenant son groupe de Galois. Il dégage à cette occasion pour la première fois, dans ce cadre, la notion de groupe, maintenant omniprésente en mathématiques. Son étude lui permet d'expliquer pourquoi les racines d'une équation prise au hasard ne s'expriment en général pas par des formules algébriques faisant intervenir ses coefficients à partir du degré 5, un résultat démontré auparavant par Abel. Plus généralement, l'étude du groupe de Galois du polynôme permet de dire exactement quand une telle formule existe. C'est ce que l'on appelle la correspondance de Galois : elle relie d'une part la théorie des corps, d'autre part la théorie des groupes.Ce cours expliquera cette théorie en n'utilisant que des résultats de base d'algèbre linéaire. Nous étudierons d'un côté la théorie des corps, c'est-à-dire la façon dont les corps s'emboîtent les uns dans les autres, en introduisant la notion de nombre algébrique (essentiellement les racines de polynômes). D'un autre côté, nous introduirons les éléments nécessaires à l'étude des groupes de permutations. Cela nous permettra d'expliquer la théorie de Galois, non seulement dans son cadre d'origine, c'est-à-dire quand les coefficients du polynôme sont des nombres entiers, mais aussi dans un cadre plus général, par exemple lorsqu'on réduit ces coefficients modulo un nombre premier p. Le cours culminera avec une comparaison des groupes de Galois dans ces deux situations (« entière » et après réduction modulo p), fournissant ainsi un outil de calcul puissant de ces groupes. Ce cours est l'occasion d'aborder des notions d'algèbre variées, essentielles dans de nombreux domaines des mathématiques, de manière très simple pour très rapidement aboutir à des résultats tout à fait remarquables. Nous n'avons pas cherché la généralité maximale mais au contraire à aller rapidement à l'essentiel en utilisant le minimum de formalisme abstrait. Le FLOTeur intéressé sera alors armé pour aller plus loin, notamment grâce à la bibliographie ou à des cours plus avancés.

Hi! Our course aims to provide necessary background in Calculus sufficient for up-following Data Science courses. Course starts with a basic introduction to concepts concerning functional mappings. Later students are assumed to study limits (in case of sequences, single- and multivariate functions), differentiability (once again starting from single variable up to multiple cases), integration, thus sequentially building up a base for the basic optimisation. To provide an understanding of the practical skills set being taught, the course introduces the final programming project considering the usage of optimisation routine in machine learning. Additional materials provided during the course include interactive plots in GeoGebra environment used during lectures, bonus reading materials with more general methods and more complicated basis for discussed themes. This Course is part of HSE University Master of Data Science degree program. Learn more about the admission into the program and how your Coursera work can be leveraged if accepted into the program here https://inlnk.ru/rj64e.

This course is all about matrices, and concisely covers the linear algebra that an engineer should know. The mathematics in this course is presented at the level of an advanced high school student, but typically students should take this course after completing a university-level single variable calculus course. There are no derivatives or integrals in this course, but students are expected to have attained a sufficient level of mathematical maturity. Nevertheless, anyone who wants to learn the basics of matrix algebra is welcome to join. The course contains 38 short lecture videos, with a few problems to solve after each lecture. And after each substantial topic, there is a short practice quiz. Solutions to the problems and practice quizzes can be found in instructor-provided lecture notes. There are a total of four weeks in the course, and at the end of each week there is an assessed quiz. Download the lecture notes: http://www.math.ust.hk/~machas/matrix-algebra-for-engineers.pdf Watch the promotional video: https://youtu.be/IZcyZHomFQc

The lectures of this course are based on the first 11 chapters of Prof. Raymond Yeung’s textbook entitled Information Theory and Network Coding (Springer 2008). This book and its predecessor, A First Course in Information Theory (Kluwer 2002, essentially the first edition of the 2008 book), have been adopted by over 60 universities around the world as either a textbook or reference text. At the completion of this course, the student should be able to: 1) Demonstrate knowledge and understanding of the fundamentals of information theory. 2) Appreciate the notion of fundamental limits in communication systems and more generally all systems. 3) Develop deeper understanding of communication systems. 4) Apply the concepts of information theory to various disciplines in information science.

As rates of change, derivatives give us information about the shape of a graph. In this course, we will apply the derivative to find linear approximations for single-variable and multi-variable functions. This gives us a straightforward way to estimate functions that may be complicated or difficult to evaluate. We will also use the derivative to locate the maximum and minimum values of a function. These optimization techniques are important for all fields, including the natural sciences and data analysis. The topics in this course lend themselves to many real-world applications, such as machine learning, minimizing costs or maximizing profits.

Algebra is one of the definitive and oldest branches of mathematics, and design of computer algorithms is one of the youngest. Despite this generation gap, the two disciplines beautifully interweave. Firstly, modern computers would be somewhat useless if they were not able to carry out arithmetic and algebraic computations efficiently, so we need to think on dedicated, sometimes rather sophisticated algorithms for these operations. Secondly, algebraic structures and theorems can help develop algorithms for things having [at first glance] nothing to do with algebra, e.g. graph algorithms. One of the main goals of the offered course is thus providing the learners with the examples of the above mentioned situations. We believe the course to contain much material of interest to both CS and Math oriented students. The course is supported by programming assignments.

In this introductory course on Ordinary Differential Equations, we first provide basic terminologies on the theory of differential equations and then proceed to methods of solving various types of ordinary differential equations. We handle first order differential equations and then second order linear differential equations. We also discuss some related concrete mathematical modeling problems, which can be handled by the methods introduced in this course. The lecture is self contained. However, if necessary, you may consult any introductory level text on ordinary differential equations. For example, "Elementary Differential Equations and Boundary Value Problems by W. E. Boyce and R. C. DiPrima from John Wiley & Sons" is a good source for further study on the subject. The course is mainly delivered through video lectures. At the end of each module, there will be a quiz consisting of several problems related to the lecture of the week.

This course is an important part of the undergraduate stage in education for future economists. It's also useful for graduate students who would like to gain knowledge and skills in an important part of math. It gives students skills for implementation of the mathematical knowledge and expertise to the problems of economics. Its prerequisites are both the knowledge of the single variable calculus and the foundations of linear algebra including operations on matrices and the general theory of systems of simultaneous equations. Some knowledge of vector spaces would be beneficial for a student. The course covers several variable calculus, both constrained and unconstrained optimization. The course is aimed at teaching students to master comparative statics problems, optimization problems using the acquired mathematical tools. Home assignments will be provided on a weekly basis. The objective of the course is to acquire the students’ knowledge in the field of mathematics and to make them ready to analyze simulated as well as real economic situations. Students learn how to use and apply mathematics by working with concrete examples and exercises. Moreover this course is aimed at showing what constitutes a solid proof. The ability to present proofs can be trained and improved and in that respect the course is helpful. It will be shown that math is not reduced just to “cookbook recipes”. On the contrary the deep knowledge of math concepts helps to understand real life situations. Do you have technical problems? Write to us: [email protected]