CS-526: Learning theoryMachine learning and data analysis are becoming increasingly central in many sciences and applications. This course concentrates on the theoretical underpinnings of machine learning.
MATH-101(f): Analysis IÉtudier les concepts fondamentaux d'analyse et le calcul différentiel et intégral des fonctions réelles d'une variable.
AR-101: Studio BA1Le cours vise à l'acquisition des outils essentiels au projet et à une compréhension de l'architecture comme savoir-faire, pensée, et attitude qui interroge l'inscription des êtres vivants dans leur e
CS-119(g): Information, Computation, CommunicationL'objectif de ce cours est d'initier les étudiants à la pensée algorithmique, de les familiariser avec les fondamentaux de
l'informatique et des communications et de développer une première compétence
CIVIL-607: Communication for Research EngineersCommunication proficiency is one of the most important results of a good PhD and postdoc experience and it is valued
equally in academia and in industry. EPFL PhD students and postdocs are expected to
MATH-207(d): Analysis IVThe course studies the fundamental concepts of complex analysis and Laplace analysis with a view to their use to solve multidisciplinary scientific engineering problems.
FIN-417: Quantitative risk managementThis course is an introduction to quantitative risk management that covers standard statistical methods, multivariate risk factor models, non-linear dependence structures (copula models), as well as p
MATH-436: Homotopical algebraThis course will provide an introduction to model category theory, which is an abstract framework for generalizing homotopy theory beyond topological spaces and continuous maps. We will study numerous
