Loosely supervised learning, RNN

This course is divided into two parts; the first will cover recurrent neural networks for processing sequential data, as well as an introduction to Transformer architectures. The second, independent part will present recent advances in weakly supervised learning: self-supervised learning (autoencoders, contrastive methods), semi-supervised learning, and generative methods.