EE-411: Fundamentals of inference and learningThis is an introductory course in the theory of statistics, inference, and machine learning, with an emphasis on theoretical understanding & practical exercises. The course will combine, and alternat
CIVIL-459: Deep learning for autonomous vehiclesDeep Learning (DL) is the subset of Machine learning reshaping the future of transportation and mobility. In this class, we will show how DL can be used to teach autonomous vehicles to detect objects,
PHYS-512: Statistical physics of computationThe students understand tools from the statistical physics of disordered systems, and apply them to study computational and statistical problems in graph theory, discrete optimisation, inference and m
DH-406: Machine learning for DHThis 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 imple
EE-566: Adaptation and learningIn 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.
HUM-256: Brain, mind and societyCe cours aborde les sujets relatifs aux recherches sur les bases cérébrales des actions humaines, en revenant notamment sur l'histoire du cerveau, les changements actuels liés au développement des neu
CS-401: Applied data analysisThis course teaches the basic techniques, methodologies, and practical skills required to draw meaningful insights from a variety of data, with the help of the most acclaimed software tools in the dat
