The aim of this course is to expose EPFL bachelor students to some of the main areas in financial economics. The course will be organized around six themes. Students will obtain both practical insights through real-world examples and understand how one can ...
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. ...
The course provides an introduction to econometrics for economics and financial applications. The objective is to learn how to make valid (i.e., causal) inference from economic and social data. ...
This course aims to introduce the basic principles of machine learning in the context of the digital humanities. We will cover both supervised and unsupervised learning techniques, and study and implement methods to analyze diverse data types, such as imag ...
After a short introduction to basic molecular biology and genomic technologies, this course covers the most useful statistical concepts and methods for the analysis of genomic data. ...
This course covers topics in applied biostatistics, with an emphasis on practical aspects of data analysis using R statistical software. Topics include types of studies and their design and analysis, high dimensional data analysis (genetic/genomic) and oth ...
This course covers statistical methods that are widely used in medicine and biology. A key topic is the analysis of longitudinal data: that is, methods to evaluate exposures, effects and outcomes that are functions of time. While motivated by real-life pro ...
Give 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 bioinformatic techniques applied by the students. Data an ...