Data Science

Understanding of fundamental notions in Machine Learning (inference, ERM and SRM principles, generalization bounds, classical learning models, unsupervised learning, semi-supervised learning.

Theoretical foundations of convex optimization.

This lecture proposes modelling problems. The problems can be industrial or academic.

The automatic processing of languages, whether written or spoken, ...

The automatic processing of languages, whether written or spoken, ...

This course is related to mathematical and statistical methods which are very used in supervised learning.

Modelling extreme temperatures, extreme river flows, earthquakes intensities, neuronal activity, map diseases, lightning strikes, forest fires, for example is a risk modelling and assessment task, which is tackled in statistics using point processes and extreme value theory.