EE-559: Deep learningThis course explores how to design reliable discriminative and generative neural networks, the ethics of data acquisition and model deployment, as well as modern multi-modal models.
MGT-502: Data science and machine learningHands-on introduction to data science and machine learning. We explore recommender systems, generative AI, chatbots, graphs, as well as regression, classification, clustering, dimensionality reduction
EE-613: Machine Learning for EngineersThe objective of this course is to give an overview of machine learning techniques used for real-world applications, and to teach how to implement and use them in practice. Laboratories will be done i
PHYS-512: Statistical physics of computationThe students understand tools from the statistical physics of disordered systems, and apply them to study computational and statistical problems in graph theory, discrete optimisation, inference and m
ME-390: Foundations of artificial intelligenceThis course provides the students with 1) a set of theoretical concepts to understand the machine learning approach; and 2) a subset of the tools to use this approach for problems arising in mechanica
