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