_index.md

# Master 2 ID3D - Analyse, Traitement d'Image et Vision 3D
# Master 2 IA - Apprentissage Machine Et Image

Teachers: [Alexandre Meyer](http://liris.cnrs.fr/alexandre.meyer), [Julie Digne](http://liris.cnrs.fr/jule.digne) et [Nicolas Bonneel](http://liris.cnrs.fr/nicolas.bonneel) - LIRIS, Université Claude Bernard Lyon 1

3 ects: 30h (CM/TP)


![MLImage_all.jpg](images/MLImage_all.jpg)

## Objective
<p style="text-align:justify;"> This page contains material for the course 'Learning and Image' in the Master of Computer Science at the University of Lyon 1 (ATIV3D for ID3D, and ‘Learning and Image’ for IA). The course takes place in autumn. The aim of the course is to provide an overview in machine learning (particularly deep learning) for image problems. The course begins by presenting the classic image-related problems, such as classification, descriptor extraction, pattern recognition, object tracking, segmentation, etc. Then, it presents generative methods. A wide range of different types of networks (CNN, auto-encoder, LSTM, GAN, transform, diffusion,etc.) is given, focusing on image data, but also on data such as point clouds, meshes, animation (skeleton), colour palettes, etc.
</p>

[For the IA Master's options, the slides are here.](doc/MLImage_PresOption.pdf)


## Topics

### Deep learning and images
  * Basis:  training, latent space, regularization, etc.
  * ConvolutionNN
  * Segmentation (U-Net, etc.)
  * Tracking (YOLO)
  * Skeleton (OpenPose, XNect, etc.)
  * Notion of transformer/attention for classification

### Generative deep learning
  * Images generation
    * auto-encoder
    * GAN
    * Diffusion
  * Extended to 3D data
  
### Deep learning and geometry
  * Geometric data
     * Point cloud (pointNet, etc.)
     * Meshes (MeshConv, etc.)
     * Diffusion on surface
  * Implicit neuronal representation (IGR, SIREN)
  * Neural radiance field NERF (Champs de radiance neuronaux)

### Optimal transport
  * Introduction to optimal transport
  


## Timetable: autumn 2024

For the IA Master's programme, classes and practical work are Thursday afternoons between October to January. For ID3D, there are classes on Tuesday mornings in September, then in October the lecturers are on Thursday afternoons and the TP on Tuesday mornings.


## Evaluation (Modalités de contrôle des connaissances)
 - Partie NB : évaluation du TP
 - Partie AM : examen papier et évaluation du TP "génération d'image à partir d'une pose"
 - Partie JD : examen papier