PHYS-467: Machine learning for physicistsMachine learning and data analysis are becoming increasingly central in sciences including physics. In this course, fundamental principles and methods of machine learning will be introduced and practi
PHYS-118: Building physicsCe cours traite des principaux phénomènes physiques observables dans le bâtiment et doit permettre à l'étudiant d'acquérir des connaissances de base dans le domaine de la physique du bâtiment.
COM-202: Signal processingSignal processing theory and applications: discrete and continuous time signals; Fourier analysis, DFT, DTFT,
CTFT, FFT, STFT; linear time invariant systems; filter design and adaptive filtering; samp
EE-512: Applied biomedical signal processingThe goal of this course is twofold: (1) to introduce physiological basis, signal acquisition solutions (sensors) and state-of-the-art signal processing techniques, and (2) to propose concrete examples
CS-214: Software constructionLearn how to design and implement reliable, maintainable, and efficient software using a mix of programming skills (declarative style, higher-order functions, inductive types, parallelism) and
fundam
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.
EE-111: Circuits and systemsCe cours présente une introduction à la théorie et aux méthodes d'analyse et de résolution des circuits électriques.
PHYS-216: Mathematical methods (for SPH)Ce cours est un complément aux cours d'analyse et d'algèbre linéaire qui apporte des méthodes et des techniques mathématiques supplémentaires requises pour les cours de physique de 3e année, notamment
ME-469: Nano-scale heat transferIn this course we study heat transfer (and energy conversion) from a microscopic perspective. First we focus on understanding why classical laws (i.e. Fourier Law) are what they are and what are their
