The MAIA Lab offers a series of courses covering theoretical aspects as well as best practices in artificial intelligence, machine learning, deep learning and software engineering. These are offered to both engineering students and computer science students.

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.

The scientific research skills within the lab cover these different aspects, as well as multimodal AI, human-agent interaction, and effective AI implementations: computational efficiency, but also more efficient learning, and more interpretable and trustable AI.