This course aims to equip students with a broad range of mathematical modelling tools and with a solid understanding of the theoretical frameworks underlying them. Students will learn to explain these frameworks and apply the corresponding tools to formulate and solve original problems in applied mathematics
Given its structure, the course will not develop in detail any particular tool or theory. Rather, its objective is to highlight a set of problem-solving techniques and tools that the student can then develop further according to his/her needs and interests in the course of his/her career (student and professional).
The course will consist of several modules focusing on mathematical modelling in different contexts and introducing a variety of tools from applied mathematics. Each module will begin with a concrete mathematical or engineering problem, providing a motivating context for the concepts and techniques to be developed. Through a participatory approach, students will explore the problem and become familiar with appropriate methods for addressing it.
In each module, particular emphasis will be placed on both the mathematical formulation and modelling of the problem and the acquisition of targeted theoretical concepts and general-purpose mathematical tools.
Examples of topics covered or revisited include information theory, game theory, finite element methods, Bayesian inference, number theory, and queuing theory.
Discrete Probability Theory [SMATB109]
The course will consist of several modules dealing with modelling in particular contexts and will address the use of various tools in applied mathematics. Each module will be introduced by a situation via a concrete mathematical or engineering problem. In a participatory approach, the students, faced with the problem, will familiarise themselves with the available resolution techniques. In each module, the emphasis will be on the one hand on the mathematical modelling of the problem posed, but on the other hand on the learning of targeted theoretical concepts and notions and on the use of general tools. Examples of concepts covered (new or revised): information theory, game theory, finite element methods, Baysian inference, stability,...
Part of the mark will be based on the student's participation in the course sessions, and in particular on the way in which he/she was able to contribute to the collective effort to solve the problems posed. Another part of the mark will be obtained in an interview (in the form of an oral examination) in which the student must be able to explain the mathematical concepts and reasoning seen in the course and to apply them in a reflective manner to new problems. All course notes and other materials will be available for consultation in preparation for this interview.
Documents are available on the course WebCampus page.
| Training | Block | Credits | Mandatory |
|---|---|---|---|
| Bachelor in Mathematics | 3 | 5 | No |