CS-250: Algorithms IThe students learn the theory and practice of basic concepts and techniques in algorithms. The course covers mathematical induction, techniques for analyzing algorithms, elementary data structures, ma
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
ME-427: Networked control systemsThis course offers an introduction to control systems using communication networks for interfacing sensors, actuators, controllers, and processes. Challenges due to network non-idealities and opportun
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 m
CS-450: Algorithms IIA first graduate course in algorithms, this course assumes minimal background, but moves rapidly. The objective is to learn the main techniques of algorithm analysis and design, while building a reper
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
MATH-261: Discrete optimizationThis course is an introduction to linear and discrete optimization.
Warning: This is a mathematics course! While much of the course will be algorithmic in nature, you will still need to be able to p
MATH-260(a): Discrete mathematicsStudy of structures and concepts that do not require the notion of continuity. Graph theory, or study of general countable sets are some of the areas that are covered by discrete mathematics. Emphasis
MATH-360: Graph theoryThe course aims to introduce the basic concepts and results of modern Graph Theory with special emphasis on those topics and techniques that have proved to be applicable in theoretical computer scienc
MATH-203(b): Analysis IIILe cours étudie les concepts fondamentaux de l'analyse vectorielle et l'analyse de Fourier en vue de leur utilisation pour
résoudre des problèmes pluridisciplinaires d'ingénierie scientifique.
MATH-476: Optimal transportThe first part is devoted to Monge and Kantorovitch problems, discussing the existence and the properties of the optimal plan. The second part introduces the Wasserstein distance on measures and devel
CS-423: Distributed information systemsThis course introduces the foundations of information retrieval, data mining and knowledge bases, which constitute the foundations of today's Web-based distributed information systems.
PHYS-117: Physics lab (metrology)Ce cours est une introduction pratique aux techniques de mesure classiques d'un laboratoire de physique ayant pour but de familiariser les étudiants avec l'acquisition de données, les capteurs, l'anal
CS-448: Sublinear algorithms for big data analysisIn this course we will define rigorous mathematical models for computing on large datasets, cover main algorithmic techniques that have been developed for sublinear (e.g. faster than linear time) data
EE-548: Audio engineeringThis 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
CS-401: Applied data analysisThis 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
COM-512: Networks out of controlThe goal of this class is to acquire mathematical tools and engineering insight about networks whose structure is random, as well as learning and control techniques applicable to such network data.
