CS-455: Topics in theoretical computer scienceThe students gain an in-depth knowledge of several current and emerging areas of theoretical computer science. The course familiarizes them with advanced techniques, and develops an understanding of f
MATH-504: Integer optimisationThe course aims to introduce the basic concepts and results of integer optimization with special emphasis on algorithmic problems on lattices that have proved to be important in theoretical computer s
COM-300: Stochastic models in communicationL'objectif de ce cours est la maitrise des outils des processus stochastiques utiles pour un ingénieur travaillant dans les domaines des systèmes de communication, de la science des données et de l'i
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-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.
CS-330: Artificial intelligenceIntroduction aux techniques de l'Intelligence Artificielle, complémentée par des exercices de programmation qui montrent les algorithmes et des exemples de leur application à des problèmes pratiques.
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 pr
MATH-403: Randomized matrix computationsThis course is concerned with randomized algorithms that have been developed during the last decade to solve large-scale linear algebra problems from, for example, scientific computing and statistica
DH-406: Machine learning for DHThis course aims to introduce the basic principles of machine learning in the context of the digital humanities. We will cover both supervised and unsupervised learning techniques, and study and imple
MATH-611: Scientific programming for EngineersThe students will acquire a solid knowledge on the processes necessary to design, write and use scientific software. Software design techniques will be used to program a multi-usage particles code, ai
CS-119(k): Information, Computation, CommunicationD'une part, le cours aborde: (1) la notion d'algorithme et de représentation de l'information, (2) l'échantillonnage d'un signal et la compression de données et (3) des aspects
liés aux systèmes: ordi
