EE-559: Deep learningThis course explores how to design reliable discriminative and generative neural networks, the ethics of data acquisition and model deployment, as well as modern multi-modal models.
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.
PHYS-114: General physics : electromagnetismLe cours traite des concepts de l'électromagnétisme, avec le support d'expériences. Les sujets traités inclus l'électrostatique, le courant électrique et circuits, la magnétostatique, l'induction élec
MATH-413: Statistics for data scienceStatistics lies at the foundation of data science, providing a unifying theoretical and methodological backbone for the diverse tasks enountered in this emerging field. This course rigorously develops
EE-110: Logic systems (for MT)Ce cours couvre les fondements des systèmes numériques. Sur la base d'algèbre Booléenne et de circuitscombinatoires et séquentiels incluant les machines d'états finis, les methodes d'analyse et de syn
EE-517: Bio-nano-chip designIntroduction to heterogeneous integration for Nano-Bio-CMOS sensors on Chip.
Understanding and designing of active Bio/CMOS interfaces powered by nanostructures.
MSE-486: Organic electronic materialsThis course will introduce students to the field of organic electronic materials. The goal of this course is to discuss the origin of electronic properties in organic materials, charge transport mecha
