| Course label : | Knowledge Engineering & Collective Intelligence |
|---|---|
| Teaching departement : | MIN / Applied Mathematics and General Computing |
| Teaching manager : | Mister PASCAL YIM |
| Education language : | French |
| Potential ects : | 0 |
| Results grid : | |
| Code and label (hp) : | LE5_9_SI_MIN_ICI - Ingé. de la conn. & int. col. |
Education team
Teachers : Mister PASCAL YIM / Mister OLIVIER BOISARD
External contributors (business, research, secondary education): various temporary teachers
Summary
Artificial intelligence has developed dramatically in recent years, particularly with deep learning technologies. The aim of this module is to introduce the main notions, with a more practical than theoretical approach.
Educational goals
At the end of the course, the student will be able to : - Process and visualize a dataset - Choose an appropriate method to analyze the data - Solving a prediction or classification problem from the data - Analyze and synthesize results Contribution of the course to the skills repository; at the end of the course, the student will have progressed in : - Ability to understand and formulate a problem - Ability to propose one or more resolution scenarios - Ability to concretize or make a prototype Working knowledge: - Processing and visualization of a set of data - Machine learning methods - Methods of deep learning Skills developed: - Analyzing a set of data - Select and implement data analysis methods Translated with www.DeepL.com/Translator (free version)
Sustainable development goals
Knowledge control procedures
Continuous Assessment
Comments:
Online resources
- Available on Moodle: datasets, MOOCs, documentation, articles
Pedagogy
- Presentation of concepts, illustration by practical demonstrations on examples - Immediate implementation - Choice of a dataset by each student (binomial) according to his interests (or in relation to PRT/PRP), and treatment of the problem as a "red thread".
Sequencing / learning methods
| Number of hours - Lectures : | 14 |
|---|---|
| Number of hours - Tutorial : | 14 |
| 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
- Basics of statistics and matrix calculation