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 pdf coming soon …

ResNet50 extra and tricks on learning
Beyond ImageNet
Segmentation (+extra on detection)

Course 7
Visual Transfer Learning: transfer and domain adaptation

Course 8
Generative models for Vision – GAN (1)

Course 9
GAN (2)++

Course 10
GAN (3)

Course 11
Visualization XAI for Autonomous Driving

Information on the exam for this course :
date : during Course 11, one hour (14:00-15:00) Jan. the 5th

link for details on these 3 last courses
Course 12
Bayesian deep learning for images
Course 13
Bayesian deep learning for images(2)
Course 14
Bayesian deep learning for images(3)

Evaluation: 4 coding eval. + one control
exemple exam2018 (FR)

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