Course 2
Introduction to Neural Networks (NNs)
Course 3
Machine Learning basics (2): (done on the blackboard) statistical decision theory and Risk minimization, ERM and regularization, connection with weight decay and maximizing the margin in SVM;
End of slides on Datasets, benchmarks and evaluation; commented examples
Course 4
Neural Nets for Image Classification
Course 5
Neural Nets for Image Classification
Vision Transformers
Course 6: Segmentation with CNN and with Transformers (and detection)
Course 7 Transfer learning and Domain adaptation
Course 8
Generative models with GANs
Course 9
Conditional GANs and diffusions models
Course 10 Control at 2pm! (all the content explained in course, not the practicals, no documents allowed)
Course 11
Introduction to foundation models: Transformers, CLIP-based Xmodal retrieval, Editing with diffusion models, Flamingo.
Course 12/13 Bayesian deep learning
Course 14 Robustness
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