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. ...
HUM-249: Competing with ChinaLe cours sensibilise les étudiant·e·s aux implications de l'émergence de la Chine comme puissance économique et technologique en portant une réflexion critique sur les défis posés par cette transition. ...
PHYS-415: Particle physics IPresentation of particle properties, their symmetries and interactions.
Introduction to quantum electrodynamics and to the Feynman rules. ...
CS-308: Introduction to quantum computationThe course introduces the paradigm of quantum computating in an axiomatic way. We introduce the notions of quantum bits, gates, and circuits. We introduce themost important quantum algorithms. We also touch upon error-correcting codes. This course is indep ...
ENV-418: River eco-morphodynamics and bioengineeringThe course deals with the interactions between hydraulics, solid transport by hauling and the watercourse space at the origin of the morphology and richness of habitats. Regime theory is presented and the quality of a stream's habitat is assessed. ...
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. ...
ME-444: HydrodynamicsNondimensionalized Navier-Stokes equations result in a great variety of models (Stokes, Lubrication, Euler, Potential) depending on the Reynolds number. The concept of boundary layer enables us then to identify the different components of the hydrodynamic ...
BIO-449: Understanding statistics and experimental designThis course is neither an introduction to the mathematics of statistics nor an introduction to a statistics program such as R. The aim of the course is to understand statistics from its experimental design and to avoid common pitfalls of statistical reason ...
MATH-451: Numerical approximation of PDEsThe course is about the derivation, theoretical analysis and implementation of the finite element method for the numerical approximation of partial differential equations in one and two space dimensions. ...
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 ...
ME-464: Introduction to nuclear engineeringThis course is intended to understand the engineering design of nuclear power plants using the basic principles of reactor physics, fluid flow and heat transfer. This course includes the following: Reactor designs, Thermal analysis of nuclear fuel, Nuclear ...
PHYS-101(a): General physics : mechanicsLe 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 ...
MATH-351: Advanced numerical analysis IIThe student will learn state-of-the-art algorithms for solving differential equations. The analysis and implementation of these algorithms will be discussed in some detail. ...
MGT-510: Performance ManagementThe objective of the course is to provide participants with the main processes and tools a company applies to evaluate the financial and non financial performance. ...
BIO-480: Neuroscience: from molecular mechanisms to diseaseThe goal of the course is to guide students through the essential aspects of molecular neuroscience and neurodegenerative diseases. The student will gain the ability to dissect the molecular basis of disease in the nervous system in order to begin to under ...
COM-516: Markov chains and algorithmic applicationsThe study of random walks finds many applications in computer science and communications. The goal of the course is to get familiar with the theory of random walks, and to get an overview of some applications of this theory to problems of interest in commu ...
CIVIL-459: Deep learning for autonomous vehiclesDeep Learning (DL) is the subset of Machine learning reshaping the future of transportation and mobility. In this class, we will show how DL can be used to teach autonomous vehicles to detect objects, make predictions, and make decisions. (Fun fact: this s ...
