This course introduces key concepts and methods for the automatic analysis and interpretation of visual content in images. Modern machine learning approaches are used to explore fundamental and advanced methods for computer vision, with emphasis on deep learning architectures and their training.
The course covers image classification, segmentation, vision–language models, and generative modeling. Topics include convolutional neural networks, Vision Transformers, self-supervised learning, diffusion models, robustness, explainability, transfer learning, and domain adaptation.
Theoretical lectures are complemented by hands-on Python practicals where students implement and experiment with the studied models.
Équipe pédagogique
- Clément Rambour
- Guillaume Jeanneret
- Pegah Khayatan
- Mathis Koroglu
- Kimia Sadreddini
Prérequis
- Basic knowledge of digital image representation
- Statistical data processing
- Scientific computing in Python
Informations
- Période: M2 S1
- Langue: anglais
- Crédits: 6 ECTS
- Code UE: UM5IN652
Attention, ce cours n’est pas un cours du parcours MIND.