FR
EN
Diagnostic et retour d'expérienceRisques et systèmes industriels et IA (RSIIA) (MRI)
Diagnostics and Feedback
Teaching unit Industrial risks and systems and AI (RSIIA) · M09O_RSI2 | EC Code M09O_RSI2
12ECTS (EU)
1Coefficient
19:30Hours
Séquencement
Learning objectives
The aim of this training is to present the basics of AI and some of its representative applications. In particular, the objective is to provide the concepts, tools and intuition needed to implement programs that can learn from data.
Program
- IA?
- Deep learning
- Automatic learning?
- the problems he tries to solve,
- main categories and basic concepts
- Learning a multilayered neural network
- European Network and Classification
- Data handling, cleaning and preparation
- Size reduction: introduction to the ACP
- Case study
Assessment
Calculator, course documents and authorized TDs.
References
- Russell & Norvig, AI: A Modern Approach, Prentice Hall (2002)
- Kevin P. Murphy - Machine Learning: A Probabilistic Perspective-The MIT Press (2012)
- Phil Kim - MatLab Deep Learning with Machine Learning, Neural Networks and Artificial Intelligence-Apress (2017)
Skills
3| Ref. | Verb | Description | Level |
|---|---|---|---|
| C1_1 | argue | Understanding what artificial intelligence is | 4 |
| C3_2 | analyze | Assess relevance of results achieved | 4 |
| C3_2 | solve | Propose a method of resolution | 4 |