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.
COM-406: Foundations of Data ScienceWe discuss a set of topics that are important for the understanding of modern data science but that are typically not taught in an introductory ML course. In particular we discuss fundamental ideas an
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-719: Digital Speech and Audio CodingThe goal of this course is to introduce the engineering students state-of-the-art speech and audio coding techniques with an emphasis on the integration of knowledge about sound production and auditor
MICRO-310(b): Signals and systems I (for SV)Présentation des concepts et des outils de base pour l'analyse et la caractérisation des signaux, la conception de systèmes de traitement et la modélisation linéaire de systèmes pour les étudiants en
COM-418: Computers and musicIn this class we will explore some of the fundamental ways in which the pervasiveness of digital devices has completely revolutionized the world of music in the last 40 years, both from the point of v
EE-490(a): Lab in acousticsApply the knowledge acquired in Electroacoustics, Audio Engineering and Propagation of Acoustic Waves lectures.
PHYS-117: Physics lab (metrology)Ce cours est une introduction pratique aux techniques de mesure classiques d'un laboratoire de physique ayant pour but de familiariser les étudiants avec l'acquisition de données, les capteurs, l'anal
ENG-366: Signals, instruments and systemsThe goal of this course is to transmit knowledge in sensing, computing, communicating, and actuating for programmable
field instruments and, more generally, embedded systems. The student will be able
MICRO-311(a): Signals and systems II (for MT)Ce cours aborde la théorie des systèmes linéaires discrets invariants par décalage (LID). Leurs propriétés et caractéristiques fondamentales y sont discutées, ainsi que les outils fondamentaux permett
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
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
