Introduction to the path integral formulation of quantum mechanics. Derivation of the perturbation expansion of Green's functions in terms of Feynman diagrams. Several applications will be presented, including non-perturbative effects, such as tunneling an ...
Machine learning and data analysis are becoming increasingly central in sciences including physics. In this course, fundamental principles and methods of machine learning will be introduced and practised. ...
In this course, students learn to design and master algorithms and core concepts related to inference and learning from data and the foundations of adaptation and learning theories with applications.
To introduce several advanced topics in quantum physics, including
semiclassical approximation, path integral, scattering theory, and
relativistic quantum mechanics ...
This course aims to introduce the basic principles of machine learning in the context of the digital humanities. We will cover both supervised and unsupervised learning techniques, and study and implement methods to analyze diverse data types, such as imag ...
Machine learning and data analysis are becoming increasingly central in many sciences and applications. In this course, fundamental principles and methods of machine learning will be introduced, analyzed and practically implemented. ...
The course offers a conceptually and methodologically advanced overview of the Standard Model and of some of its extensions. It provides the students with the basic tools and with the first elements of "Beyond the SM" model-building and phenomenology. ...
Supersymmetry is the unique quantum extension of the symmetry principles of relativity.
This course offers a first but broad introduction covering the role of Supersymmetry in our understanding of both physics beyond the Standard Model and non-perturbative ...