The school will open the thematic period on Data Science and will be dedicated to the mathematical foundations and methods for high-dimensional data analysis. It will provide an in-depth introduction to key mathematical techniques that underpin modern approaches to machine learning and statistical inference. Topics covered will include optimization methods for machine learning, generative modeling and sampling, recurrent neural networks, and high-dimensional Bayesian inference. The school aims to offer students and early-career researchers a solid grounding in these areas, presenting both theoretical developments and practical applications. Poster sessions will provide an opportunity for PhD students and postdocs to share their research and foster new collaborations.

Registration is free but mandatory. Please fill the registration form in order to confirm your participation.