Teaching activities

The courses offered in artificial intelligence and machine learning cover:

  • advances in artificial intelligence through deep learning applied to long-term dependency modeling, and in particular to natural language processing. The exploitation of DNNs, CNNs, RNNs, LSTMs, GRUs, Attention, Transformers, GPTs, generative models, GANs, and auto-encoders allow applications such as machine translation, information classification and extraction, chatbots, the extraction and search for information in “big data”, etc.
  • the field at the intersection of artificial intelligence and probability theory, including probabilistic graphical models, Bayesian networks, and their implementation via probabilistic programming. Decision-making in the face of uncertainty (missing information, noisy data, etc.) via statistical inference is one of the major contributions of probabilistic AI.