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
MGT-418: Convex optimizationThis course introduces the theory and application of modern convex optimization from an engineering perspective. ...
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.) ...
PHYS-642: Statistical physics for optimization & learningThis course covers the statistical physics approach to computer science problems, with an emphasis on heuristic & rigorous mathematical technics, ranging from graph theory and constraint satisfaction to inference to machine learning, neural networks and st ...
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 ...
ENG-209: Data science for engineers with 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 ...
MATH-234(b): Probability and statisticsLe cours présente les notions de base de la théorie des probabilités et de l'inférence statistique. L'accent est mis sur les concepts principaux ainsi que les méthodes les plus utilisées. ...
MICRO-462: Learning and adaptive control for robotsTo cope with constant and unexpected changes in their environment, robots need to adapt their paths rapidly and appropriately without endangering humans. this course presents method to react within millisecunds. ...
EE-311: Fundamentals of machine learningCe cours présente une vue générale des techniques d'apprentissage automatique, passant en revue les algorithmes, le formalisme théorique et les protocoles expérimentaux. ...
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. ...
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. ...
BIO-378: Physiology lab ILe TP de physiologie introduit les approches expérimentales du domaine biomédical, avec les montages de mesure, les capteurs, le conditionnement des signaux, l'acquisition et traitement de données.
Les résultats physiologiques finaux illustrent le contenu ...
BIO-379: Physiology lab IILe TP de physiologie introduit les approches expérimentales du domaine biomédical, avec les montages de mesure, les capteurs, le conditionnement des signaux, l'acquisition et traitement de données.
Les résultats physiologiques finaux illustrent le contenu ...
EE-209: Elements of statistics for data scienceThéorie de l'estimation: estimateurs du maximum de vraisemblance, information de Fisher, inégalité de Cramer-Rao, intervalles de confiance.
Tests d'hypothèses: cadre de Neyman-Pearson: test du rapport de vraisemblance, tests paramétriques et non-parmétriq ...
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. ...
COM-308: Internet analyticsInternet analytics is the collection, modeling, and analysis of user data in large-scale online services, such as social networking, e-commerce, search, and advertisement. This class explores a number of the key functions of such online services that have ...
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.
