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