| Course label : | Artificial intelligence |
|---|---|
| Teaching departement : | CMA / |
| Teaching manager : | Mister JEAN-MARC FOUCAUT / Mister JORAN ROLLAND |
| Education language : | |
| Potential ects : | 2 |
| Results grid : | |
| Code and label (hp) : | MR_TUR_CMA_AIN - Artificial intelligence |
Education team
Teachers : Mister JEAN-MARC FOUCAUT / Mister JORAN ROLLAND
External contributors (business, research, secondary education): various temporary teachers
Summary
Machine Learning and artificial intelligence are being more and more used in many areas of science and technology and have of course received some attention from the fluid mechanics and turbulence community. A fluid dynamicist should therefore have a clear idea of what these tools are, whether they should be used for his/her purpose and how can they be used. He should of course also know that machine learning does not necessarily equates to neural networks. This course will therefore be a short introduction to machine learning and its use in turbulence. The central principles of machine learning will be reminded and the main methods will be introduced. Recent applications to turbulence will be presented. Finally, some practices using pythons will be proposed
Educational goals
At the end of the course the student should be able to - Know the main principles of machine learning and data based methods. - Know the main types of machine learning (supervised/unsupervised/reinforcement learning etc.) and the main types of algorithms (nearest neighbours, parametric models, neural networks etc.) - Know some of the recent applications of machine learning in turbulence - Use a machine learning library on python to perform fundamental tasks
Sustainable development goals
Knowledge control procedures
Final Exam
Comments:
Online resources
Lecture notes and transparencies Text of practices
Pedagogy
Lectures, computer practices
Sequencing / learning methods
| Number of hours - Lectures : | 20 |
|---|---|
| Number of hours - Tutorial : | 0 |
| 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
Numerical analysis course, mathematics course, programming course, fluid mechanics course, turbulence course