Course

PHYS-421: Physics project I

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PHYS-422: Physics project II

L'étudiant.e applique les compétences acquises au cours de ses études dans une recherche effectuée dans l'un des laboratoires de la section de physique sous l'encadrement d'un.e enseignant.e de la sec

CS-411: Digital education

This course addresses the relationship between specific technological features and the learners' cognitive processes. It also covers the methods and results of empirical studies: do student actually l

BIO-455: Introduction to law and ethics

Le but du cours est de familiariser l'étudiant-e aux notions de base du droit et de l'éthique applicables à la recherche en LSE et à son transfert en applications, et de lui fournir les éléments essen

PHYS-512: Statistical physics of computation

The 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 m

CS-401: Applied data analysis

This course teaches the basic techniques, methodologies, and practical skills required to draw meaningful insights from a variety of data, with the help of the most acclaimed software tools in the dat

MATH-412: Statistical machine learning

A course on statistical machine learning for supervised and unsupervised learning

HUM-432: How people learn: Designing Learning Tools I

The students will understand the cognitive and social factors which affect learning - particularly in science and engineering. They will be able to use social research techniques as part of the design

ENG-209: Data science for engineers with Python

Ce cours est divisé en deux partie. La première partie présente le langage Python et les différences notables entre Python et C++ (utilisé dans le cours précédent ICC). La seconde partie est une intro

MICRO-330: Sensors

Principes physiques et électronique utilisés dans les capteurs. Applications des capteurs.

EE-311: Fundamentals of machine learning

Ce cours présente une vue générale des techniques d'apprentissage automatique, passant en revue les algorithmes, le formalisme théorique et les protocoles expérimentaux.