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
MGT-416: Causal inferenceStudents will learn the core concepts and techniques of network analysis with emphasis on causal inference. Theory and
application will be balanced, with students working directly with network data th
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.
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 pr
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 imple
