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-131: 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.
MATH-562: Statistical inferenceInference from the particular to the general based on probability models is central to the statistical method. This course gives a graduate-level account of the main ideas of statistical inference.
PHYS-442: Modeling and design of experimentsIn the academic or industrial world, to optimize a system, it is necessary to establish strategies for the experimental approach. The DOE allows you to choose the best set of measurement points to min
FIN-417: Quantitative risk managementThis course is an introduction to quantitative risk management that covers standard statistical methods, multivariate risk factor models, non-linear dependence structures (copula models), as well as p
MATH-516: Applied statisticsThe course will provide an overview of everyday challenges in applied statistics through case studies. Students will learn how to use core statistical methods and their extensions, and will use comput
ChE-403: Heterogeneous reaction engineeringThe theoretical background and practical aspects of heterogeneous reactions including the basic knowledge of heterogeneous catalysis are introduced. The fundamentals are given to allow the design of m
MATH-240: StatisticsCe cours donne une introduction au traitement mathématique de la théorie de l'inférence statistique en utilisant la notion de vraisemblance comme un thème central.
ME-469: Nano-scale heat transferIn this course we study heat transfer (and energy conversion) from a microscopic perspective. First we focus on understanding why classical laws (i.e. Fourier Law) are what they are and what are their
BIO-603(LG): Practical - LaManno LabGive students a feel for how single-cell genomics datasets are analyzed from raw data to data interpretation. Different steps of the analysis will be demonstrated and the most common statistical and b
MATH-522: Empirical processesFrom prototypical examples of estimators used by statisticians, to more complex nonparametric models, methods and theorems will be taught to study their (non)asymptotic behavior, when defined as mappi
MATH-336: Randomization and causationThis course covers formal frameworks for causal inference. We focus on experimental designs, definitions of causal models, interpretation of causal parameters and estimation of causal effects.
MATH-413: Statistics for data scienceStatistics lies at the foundation of data science, providing a unifying theoretical and methodological backbone for the diverse tasks enountered in this emerging field. This course rigorously develops
