COM-406: Foundations of Data ScienceWe discuss a set of topics that are important for the understanding of modern data science but that are typically not taught in an introductory ML course. In particular we discuss fundamental ideas and techniques that come from probability, information the ...
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-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-411: Digital educationThis course addresses the relationship between specific technological features and the learners' cognitive processes. It also covers the methods and results of empirical studies: do student actually learn due to technologies? In fall 2025, P. Dillenbourg w ...
MATH-414: Stochastic simulationThe student who follows this course will get acquainted with computational tools used to analyze systems with uncertainty arising in engineering, physics, chemistry, and economics. Focus will be on sampling methods as Monte Carlo, quasi Monte Carlo, Mark ...
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-119(h): Information, calcul, communicationL'objectif de ce cours est d'initier les étudiants à la pensée algorithmique, de les familiariser avec les fondamentaux de l'informatique et des communications et de développer une première compétence en programmation avec le langage Python. ...
EE-429: Fundamentals of VLSI designThe course introduces the fundamentals of digital integrated circuits and the technology aspects from a designers perspective. It focuses mostly on transistor level, but discusses also the extension to large digital semicustom designs. ...
MATH-442: Statistical theoryThis course gives a mostly rigourous treatment of some statistical methods outside the context of standard likelihood theory. ...
AR-211: StéréotomieLa Stéréotomie est l'art de concevoir et fabriquer des volumes complexes en pierre et des assemblages en bois. Ce cours propose une réinterprétation de la Stéréotomie avec différents outils, une réflexion sur la géométrie, les processus de fabrication, les ...
MATH-493: Applied biostatisticsThis 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 ...
MATH-474: Statistics for genomic data analysisAfter 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. ...
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 ...
