CS-491: Introduction to IT consultingThis course is an introduction to the alignment of enterprise needs with the possibilities offered by Information Technology (IT). Using a simulated business case, we explore how to define the require
MATH-502: Distribution and interpolation spacesThe goal of this course is to give an introduction to the theory of distributions and cover the fundamental results of Sobolev spaces including fractional spaces that appear in the interpolation theor
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.
BIO-373: Genetics and genomicsThe theoretical part of this course covers classical genetics and contemporary genomics. Because bioinformatics has become important for genomic research, the course also includes practical applicatio
CS-213: Human computer interactionLa discipline de l'Interaction Homme-Machine (ou HCI : Human-Computer Interaction) vise à systématiquement placer le facteur humain dans la conception de systèmes interactifs.
PHYS-207(a): General physics : quantaCe cours est une introduction à la mécanique quantique. En partant de son développement historique, le cours traite les notions de complémentarité quantique et le principe d'incertitude, le processus
MGT-416: Causal inferenceStudents will learn the core concepts and techniques of network analysis with emphasis on causal inference. Theory and
application will be balanced, with students working directly with network data th
COM-405: Mobile networksThis course provides a detailed description of the organization and operating principles of mobile and wireless communication networks, as well as the use of wireless signals for sensing and imaging.
CS-550: Formal verificationWe introduce formal verification as an approach for developing highly reliable systems. Formal verification finds proofs that computer systems work under all relevant scenarios. We will learn how to u
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.
