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Concept

Alignement des intelligences artificielles

Cours associés (13)

DH-415: Ethics and law of AI

This master course enables students to sharpen their proficiency in tackling ethical and legal challenges linked to Artificial Intelligence (AI). Students acquire the competence to define AI and identify ethical and legal questions linked to its conception ...

MATH-101(en): Analyse I (anglais)

We study the fundamental concepts of analysis, calculus and the integral of real-valued functions of a real variable. ...

EE-556: Mathematics of data: from theory to computation

This course provides an overview of key advances in continuous optimization and statistical analysis for machine learning. We review recent learning formulations and models as well as their guarantees

PHYS-431: Quantum field theory I

The goal of the course is to introduce relativistic quantum field theory as the conceptual and mathematical framework describing fundamental interactions. ...

MATH-115(a): Algèbre linéaire avancée II - diagonalisation

L'objectif du cours est d'introduire les notions de base de l'algèbre linéaire et de démontrer rigoureusement les résultats principaux de ce sujet. ...

CS-500: AI product management

The course focuses on the development of real-word AI/ML products. It is intended for students who have acquired a theoretical background in AI/ML and are interested in applying that toward developing AI/ML-oriented products. ...

ME-324: Commande numérique des systèmes dynamiques

On introduit les bases de l'automatique linéaire discrète qui consiste à appliquer une commande sur des intervalles uniformément espacés. La cadence de l'échantillonnage qui est associée joue un rôle primordial pour assurer la stabilité et la performance d ...

MATH-232: Probabilités et statistique (pour IC)

Cours d'introduction à la théorie des probabilités et aux statistiques. ...

PHYS-101(d): Physique générale : mécanique

Le 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-467: Probabilistic methods in combinatorics

The 'probabilistic method' is a fundamental tool in combinatorics. The basic idea is as follows: to prove that an object (for example, graph) with certain properties exists, it suffices to prove that if the object is chosen at random, then it has the desir ...

HUM-392: The Ethics and Law of Artificial Intelligence

This course enables students to sharpen their proficiency in tackling ethical and legal challenges linked to Artificial Intelligence (AI). Students acquire the competence to define AI and identify ethical and legal questions linked to its increased use in ...

CS-456: Deep reinforcement learning

This course provides an overview and introduces modern methods for reinforcement learning (RL.) The course starts with the fundamentals of RL, such as Q-learning, and delves into commonly used approaches, like PPO and DQN. The course will introduce student ...

HUM-384: The ethics of robots

This course enables students to sharpen their proficiency in tackling ethical challenges linked to robots. Students acquire the competence to define a robot and identify ethical and legal questions linked to technology and to the increased use of robots in ...