Info about practicals

Course 1
Visual Representation of images Bag of Features and Bag of Words

Course 2
Supervised Learning: Neural Net architectures

Course 3
Supervised Learning: theory and practices, SVM

Course 4
Neural Nets for Image Classification

Course 5
Large scale convolutional neural nets

Course 6

Extras on ResNet50 and tricks for learning
Beyond ImageNet: from fully convolutional to segmentation deep nets

Course 7
Visual Transfer Learning: transfer and domain adaptation

Course 8
Transformers for vision

Course 9
Generative models for Vision – GAN (1)
GAN (2)

Course 10
GAN (3)

Course 11 Jan. the 5th

exam (only about the 10 first weeks of the course (all the content explained in course, not the practicals)
no documents allowed

exemple exam2018 (FR)
This control is completed by 3 evaluations during practicals (over the 11 first weeks) and 1 for the 3 last weeks

link for details on these 3 last courses

Course 12: Bayesian Models (January, 12th 2022)
Practical session (Jupyter notebook version)  


Course 13: Bayesian Neural Networks (January, 26th 2022)    
Practical session (Jupyter notebook version)  


Course 14: Bayesian Deep Learning and Robustness (February, 2nd 2022)  
Practical session (Jupyter notebook version)   

Further reading (available at SorbonneU library):
Book Pattern Recognition and Machine Learning, C. M. Bishop
Book Deep Learning, I. Goodfellow, Y. Bengio, A. Courville
Book Computer Vision: Algorithms and Applications, Richard Szeliski