BIOENG-210: Biological data science I: statistical learningProcessing, analyzing, and interpreting large biological datasets is an essential skill for modern biologists. This course aims to provide the theoretical foundations, analytical techniques, and software tools necessary to effectively manage and derive ins ...
MATH-341: Linear modelsRegression modelling is a fundamental tool of statistics, because it describes how the law of a random variable of interest may depend on other variables. This course aims to familiarize students with linear models and some of their extensions, which lie a ...
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-233: Introduction to machine learningMachine learning and data analysis 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. ...
MGT-581: Introduction to econometricsThe 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. ...
BIO-369: Randomness and information in biological dataBiology 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. ...
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. ...
DH-406: Machine learning for DHThis 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 ...
MATH-449: BiostatisticsThis 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 ...
FIN-403: EconometricsThe course covers basic econometric models and methods that are routinely applied to obtain inference results in economic and financial applications. ...
FIN-604: Financial Econometrics IWe provide a comprehensive overview of the econometric tools that are essential to estimate financial models, both for asset pricing and
for corporate finance. ...
MGT-448: Statistical inference and machine learningThis course aims to provide graduate students a thorough grounding in the methods, theory, mathematics and algorithms needed to do research and applications in machine learning. The course covers topics from machine learning, classical statistics, and data ...
CS-233(b): Introduction to machine learning (BA4)Machine learning and data analysis 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-233(a): Introduction to machine learning (BA3)Machine learning and data analysis 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. ...
MATH-710: Data Analysis for Science and EngineeringAn overview course intended for scientists and engineers who need to use statistical methods as part of their research, who have already attended a course at the second-year EPFL undergraduate level, and need revision and deepening of their knowledge at a ...
