MATH-131: Probabilités et statistiqueLe cours présente les notions de base de la théorie des probabilités et de l'inférence statistique. L'accent est mis sur les concepts principaux ainsi que les méthodes les plus utilisées. ...
EE-465: Industrial electronics IThe course deals with the control of grid connected power electronic converters for renewable applications, covering: converter topologies, pulse width modulation, modelling, control algorithms and controllers (PID and PR), coordinate frame transformations ...
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 practised. ...
PHYS-512: Statistical physics of computationThe students understand tools from the statistical physics of disordered systems, and apply them to study computational and statistical problems in graph theory, discrete optimisation, inference and machine learning. ...
ME-443: Hydroacoustic for hydropower plantsIntroduction to pressure wave propagation phenomena in hydraulic circuits, water hammer calculations, transient behaviour of hydroelectric plants, 1D numerical simulation of the dynamic behaviour of Francis, Pelton and Kaplan turbines, and study of the sta ...
EE-536: Physical models for micro and nanosystemsStudents will learn simple theoretical models, the theoretical background of finite element modeling as well as its application to modeling charge, mass and heat transport in electronic, fluidic and e
EE-548: AudioThis lecture is oriented towards the study of audio engineering, room acoustics, sound propagation, and sound radiation from sources and acoustic antennas. The learning outcomes will be the techniques for microphones and loudspeaker design, as well as room ...
PHYS-101(a): Physique générale : mécaniqueLe but du cours de physique générale est de donner à l'étudiant les notions de base nécessaires à la compréhension des phénomènes physiques. L'objectif est atteint lorsque l'étudiant est capable de prévoir quantitativement les conséquences de ces phénomène ...
MSE-213: Probability and statistics for materials scienceThe course establishes the concepts of statistical analysis, probability theory, and data analysis. This includes standard statistical tests, correlation analysis and experimental design. It introduces computational statistical methods to analyse large dat ...
MATH-442: Statistical theoryThis course gives a mostly rigourous treatment of some statistical methods outside the context of standard likelihood theory. ...
EE-566: Adaptation and learningIn this course, students learn to design and master algorithms and core concepts related to inference and learning from data and the foundations of adaptation and learning theories with applications.
EE-348: ElectroacoustiqueCe cours a pour objectif de former les étudiants de section Génie Electrique et Electronique à la conception de systèmes acoustiques, à l'aide d'un formalisme basé sur l'électrotechnique. A la fin du semestre, les étudiants seront capables de dimensionner, ...
CS-433: Machine learningMachine learning methods are becoming increasingly central in many sciences and applications. In this course, fundamental principles and methods of machine learning will be introduced, analyzed and practically implemented. ...
ENG-267: Méthodes d'estimationLes étudiants traitent des observations entachées d'incertitude de manière rigoureuse. Ils maîtrisent les principales méthodes de compensation des mesures et d'estimation des paramètres. Ils appliquent certains modèles à des problèmes concrets issus de div ...
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
fundamental software construction concepts (reusability, abstr ...
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 and techniques that come from probability, information the ...
