This course focuses on dynamic models of random phenomena, and in particular, the most popular classes of such models: Markov chains and Markov decision processes. We will also study applications in queuing theory, finance, project management, etc. ...
We learn how to control the nonasymptotic and random behavior of collections of estimators, when indexed by classes of functions/sets. Examples range from prototypical estimators used by statisticians, to nonparametric models used in machine learning. ...
Le 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. ...
Discrete mathematics is a discipline with applications to almost all areas of study. It provides a set of indispensable tools to computer science in particular. This course reviews (familiar) topics as diverse as mathematical reasoning, combinatorics, disc ...
Ce cours donne une introduction au traitement mathématique de la théorie de l'inférence statistique en utilisant la notion de vraisemblance comme un thème central. ...
This course gives an introduction to the assessment of risk with a particular focus on modelling of rare events, which can have huge environmental impacts. ...
This 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 ...
Biology is becoming more and more a data science, as illustrated by the explosion of available genome sequences. This course aims to show how we can make sense of such data and harness it in order to understand biological processes in a quantitative way. ...
Inference from the particular to the general based on probability models is central to the statistical method. This course gives a graduate-level introduction of the main ideas of statistical inference. ...
This course provides in-depth understanding of the most fundamental algorithms in statistical pattern recognition or machine learning (including Deep Learning) as well as concrete tools (as Python source code) to PhD students for their work. ...
We provide a comprehensive overview of the econometric tools that are essential to estimate financial models, both for asset pricing and
for corporate finance. ...