Code
IGSF IMA 4201
Level
M1
Graduate
Graduate
Semester
Fall
Domain
Image
Program
Programme Ingénieur
Language
Anglais/English
ECTS Credits
2,5
Class hours
25
Workload
50
Program Manager(s)
Department
- Advanced Research and Techniques for Multidimensional Imaging Systems
Educational team
Organisation
Cours/TD/TP/projet/examen : 9/3/9/4Learning objectives
At the end of this module, the student will be able to:
- master the principles and the main architectures of deep neural networks used in image processing/analysis
-take in hand and deploy the main development tools and software libraries of artificial intelligence solutions available today (Tensorflow, PyTorch ...)
- appropriate and experiment some existing artificial intelligence solutions
CDIO Skills
- 1.3 - Advanced engineering fundamental knowledge, methods and tools
- 2.1.2 - Modeling
- 2.2 - Experimentation, investigation and knowledge discovery
- 2.4.3 - Creative Thinking
- 3.1.2 - Team Operation
Prerequisites
Aucun
Content
The objective is to discover the fundamental methods and techniques of artificial intelligence and in particular deep learning, in the context of computer vision. After an introduction setting out the fundamental theoretical principles, the different types of networks will be discovered. Withinthis context, the student will be required to install and configure popular neural network deployment platforms (Tensorflow, Pytorch...), then experiment and evaluate basic approaches.
Evaluation
Final note = Oral presentation of a topic proposed by the teaching team
Assessment formula
Note finale = Présentation orale d'un sujet proposé par l'équipe enseignante
References
- Polycopiés et bibliographie spécifiques remis par les intervenants
- Ian Goodfellow, Yoshua Bengio, Aaron Courville, “Deep Learning”, MIT Press.
-Les MOOC machine learning et deep learning