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Cours associés (29)

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EE-452: Network machine learning

Fundamentals, methods, algorithms and applications of network machine learning and graph neural networks

COM-516: Markov chains and algorithmic applications

The 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 appl

MATH-448: Statistical analysis of network data

A first course in statistical network analysis and applications.

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

AR-219: Advanced CAO and Integrated Modeling DIM

1ère année: bases nécessaires à la représentation informatique 2D (3D). Passage d'un à plusieurs logiciels: compétence de choisir les outils adéquats en 2D et en 3D. Mise en relation des outils de CAO

MGT-416: Causal inference

Students will learn the core concepts and techniques of network analysis with emphasis on causal inference. Theory and application will be balanced, with students working directly with network data th

EE-626: Graph representations for biology and medicine

Systems of interacting entities, modeled as graphs, are pervasive in biology and medicine. The class will cover advanced topics in signal processing and machine learning on graphs and networks, and wi

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

ME-427: Networked control systems

This 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

EE-619: Advanced topics in network neuroscience

The main goal of this course is to give the student a solid introduction into approaches, methods, and tools for brain network analysis. The student will learn about principles of network science and