Lecturer: Prof.ssa Rita Fioresi (UniBo)
Duration: 12 hours
Learning outcomes: Introduction to Deep Learning, basic steps of the algorithm analogies with the human visual systems and its mathematical models. The geometry of the space of data and the space of parameters; KL divergence and its information geometry interpretation. Geometric Deep Learning: the algorithm of Deep Learning on Graphs. Message passing and GATs: a geometrical modelling via heat equation and laplacian on graphs. This course will be self-contained as much as possible. The necessary differential geometric concepts (manifolds, Frobenius theorem, Cartan formalism) will be introduced and explained. The necessary programming skills will NOT be assumed, but a part of the course will be "hands on" illustrating key examples on colab.
Exam: The exam will consist in a brief exposition of some concepts and the students can choose the part of the program they like the most and present a focused exposition on one argument.
Period: Dec 2025 - Jan 2026
Dates: look here