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.
ME-373: Finite element modelling and simulationL'objectif de ce cours est d'apprendre à réaliser de manière rigoureuse et critique des analyses par éléments finis de problèmes concrets en mécanique des solides à l'aide d'un logiciel CAE moderne.
ME-474: Numerical flow simulationThis course provides practical experience in the numerical simulation of fluid flows. Numerical methods are presented in the framework of the finite volume method. A simple solver is developed with Ma
PHYS-432: Quantum field theory IIThe goal of the course is to introduce relativistic quantum field theory as the conceptual and mathematical framework describing fundamental interactions such as Quantum Electrodynamics.
MATH-351: Advanced numerical analysis IIThe student will learn state-of-the-art algorithms for solving differential equations. The analysis and implementation of these algorithms will be discussed in some detail.
CS-433: Machine learningMachine learning methods are becoming increasingly central in many sciences and applications. In this course, fundamental principles and methods of machine learning will be introduced, analyzed and pr
MATH-203(b): Analysis IIILe cours étudie les concepts fondamentaux de l'analyse vectorielle et l'analyse de Fourier en vue de leur utilisation pour
résoudre des problèmes pluridisciplinaires d'ingénierie scientifique.
EE-536: Physical models for micro and nanosystemsStudents will learn simple theoretical models, the theoretical background of finite element modeling as well as its application to modeling charge, mass and heat transport in electronic, fluidic and e
