| Course label : | Probabilities |
|---|---|
| Teaching departement : | MIN / Applied Mathematics and General Computing |
| Teaching manager : | Mister KHALED MESGHOUNI |
| Education language : | French |
| Potential ects : | 0 |
| Results grid : | |
| Code and label (hp) : | LE1_2_MA_MIN_PRO - Probabilités |
Education team
Teachers : Mister KHALED MESGHOUNI
External contributors (business, research, secondary education): various temporary teachers
Summary
The purpose of this teaching is to provide the necessary and essential knowledges for understanding and using of probabilities, and probabilistic reasoning. These probabilistic methods are useful in all areas of engineering sciences where random and experimental aspects appear. Furthermore, these knowledges are a prerequisite for courses in operational research and statistics.
Educational goals
At the end of the course, the student will be able to: - Understand and model the random aspects of processes or services - Represent random experiences and know how to calculate the probabilities of associated events - Use the usual probability laws to model and make predictions - Analyze and understand data from sampling Contribution of the course to the competency framework: At the end of the course, students will have progressed in their ability to: - Conduct risk analyzes, and implement quality procedures, (Conduct statistical analysis, understand and analyze feedback, make decisions based on uncertain data) - Develop relevant strategies for resolving complex technical issues, (Increase the company's performance, develop production by respecting quality standards and ensuring customer satisfaction) - Communicate with customers, suppliers, principals, stakeholders Knowledge worked: - First and second axiom of probabilities - Total Probability and Probability of Causes (Bayes) - Discrete and continuous random variables - Expectation and variance - Laws of discrete and continuous probabilities, - Random vector - Treatment of a sample Skills developed : - To know how to model a problem, to know how to implement adapted resolution techniques thanks to its control of statistical methods - Knowing how to synthesize datasets, how to extract information from observations - Know how to make decisions based on uncertain data, assess risks, move from risks to issues,
Sustainable development goals
Knowledge control procedures
Continuous Assessment / Final Exam
Comments: Final assessment.
Online resources
Excel for using the most common probability laws. The Moodle pedagogical platform is used for the delivery of courses and exercises, continuous and final assessment.
Pedagogy
The focus is on problem solving and the study of the most common random phenomena.
Sequencing / learning methods
| Number of hours - Lectures : | 0 |
|---|---|
| Number of hours - Tutorial : | 18 |
| Number of hours - Practical work : | 0 |
| Number of hours - Seminar : | 0 |
| Number of hours - Half-group seminar : | 0 |
| Number of student hours in TEA (Autonomous learning) : | 0 |
| Number of student hours in TNE (Non-supervised activities) : | 0 |
| Number of hours in CB (Fixed exams) : | 0 |
| Number of student hours in PER (Personal work) : | 0 |
| Number of hours - Projects : | 0 |
Prerequisites
Some notions of higher mathematics (partial derivatives, double integrals).
Maximum number of registrants
Remarks
The contributions to the competency framework and the skills developed are deepened in the second year by the course on industrial statistics. A test is organised in the middle of the cursus for self-evaluation purpose.