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 practised. ...
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 practically implemented. ...
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 data science world (pandas, scikit-learn, Spark, etc.) ...
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.
ME-390: Foundations of artificial intelligenceThis course provides the students with 1) a set of theoretical concepts to understand the machine learning approach; and 2) a subset of the tools to use this approach for problems arising in mechanical engineering applications. ...
EE-411: Fundamentals of inference and learningThis is an introductory course in the theory of statistics, inference, and machine learning, with an emphasis on theoretical understanding & practical exercises. The course will combine, and alternate, between mathematical theoretical foundations and prac ...
FIN-407: Machine learning in financeThis course aims to give an introduction to the application of machine learning to finance, focusing on the problems of portfolio optimization, return prediction, and textual analysis. A particular focus will be on deep learning and the practical details o ...
MICRO-455: Machine learning IReal-world engineering applications must cope with a large dataset of dynamic variables, which cannot be well approximated by classical or deterministic models. This course gives an overview of methods from Machine Learning for the analysis of non-linear, highly noisy and multi dimensional data
ENG-209: Data science pour ingénieurs avec PythonCe cours propose une immersion progressive et complète dans le domaine de la Data Science à travers le langage Python. Il guide les étudiants depuis la manipulation de données brutes jusqu'à la modélisation et l'extraction de connaissances utiles à partir ...
PHYS-754: Lecture series on scientific machine learningThis lecture presents ongoing work on how scientific questions can be tackled using machine learning. Machine learning enables extracting knowledge from data computationally and in an automatized way. We will learn on examples how this is influencing the v ...
CS-503: Visual intelligenceThis course covers both classic concepts and recent advances in computer vision and machine learning for processing visual data -- with a primary focus on embodied intelligence and multimodal learning ...
CS-119(c): Information, calcul, communicationL'objectif de ce cours est d'introduire les étudiants à la pensée algorithmique, de les familiariser avec les fondamentaux de l'Informatique et de développer une première compétence en programmation (langage C++). ...
EE-559: Deep learningThis course explores how to design reliable discriminative and generative neural networks, the ethics of data acquisition and model deployment, as well as modern multi-modal models. ...
DH-405: Foundations of digital humanitiesThis course introduces the core concepts and methodologies of Digital Humanities, integrating both theoretical and practical perspectives. Students learn to work with large-scale cultural datasets, acquiring the tools and techniques required for their proc ...
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 machine learning. ...
CS-411: Digital educationThis 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 learn due to technologies? In fall 2025, P. Dillenbourg w ...
COM-490: Large-scale data science for real-world dataThis hands-on course covers tools and methods used by data scientists, from researching solutions to scaling prototypes on Spark clusters. Students engage with the full data engineering and data science pipeline, from data acquisition to extracting insight ...
