MICRO-310(a): Signals and systems I (for MT)Présentation des concepts et des outils de base pour la caractérisation des signaux ainsi que pour l'analyse et la synthèse des systèmes linéaires (filtres ou canaux de transmission). Application de c
EE-296: Electrical systems and electronics IILes concepts de base permettant de comprendre et d'analyser les systèmes électroniques dédiés à l'acquisition et au traitement des signaux (signaux physiologique, bio-capteurs) seront abordés en théor
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
ME-326: Control systems and discrete-time controlCe cours inclut la modélisation et l'analyse de systèmes dynamiques, l'introduction des principes de base et l'analyse de systèmes en rétroaction, la synthèse de régulateurs dans le domain fréquentiel
CS-423: Distributed information systemsThis course introduces the foundations of information retrieval, data mining and knowledge bases, which constitute the foundations of today's Web-based distributed information systems.
FIN-616: Financial Econometrics II (2020 -2024)This course has 3 parts
- We understand how to use moment based estimations to obtain the parameters for explicit or implicit models.
- We learn how to estimate latent parameters in a time series cont
MATH-660: Numerical methods for data assimilationThis course will review modern techniques for parameter and state estimation in a Bayesian framework for models involving differential equations, with particular attention to the high dimensional sett
EE-550: Image and video processingThis course covers fundamental notions in image and video processing, as well as covers most popular tools used, such as edge detection, motion estimation, segmentation, and compression. It is compose
EE-726: Sparse stochastic processesWe cover the theory and applications of sparse stochastic processes (SSP). SSP are solutions of differential equations driven by non-Gaussian innovations. They admit a parsimonious representation in a
