Info about practicals

Course 1
Computer Vision and ML basics: Visual (local) feature detection and description, Bag of Word Image representation, Linear classification (SVM)

Course 2
We are finishing course 1 from SVM to datasets and
Introduction to Neural Networks (NNs)

Course 3
Machine Learning basics (2): Risk, Classification, Datasets, benchmarks and evaluation
Course 4
Neural Nets for Image Classification
Course 5
Vision Transformers
Course 6
Transfer learning and domain adaptation
Course 7
Segmentation and Detection
Course 8
Generative models with GANs
Course 9
Generative models with diffusion
Course 10
Large VL models: CLIP, StableDiffusion, Flamingo
Course 11
Control (to be checked) – Explainable AI, Fairness

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