ENG-209: Data science for engineers with PythonCe cours est divisé en deux partie. La première partie présente le langage Python et les différences notables entre Python et C++ (utilisé dans le cours précédent ICC). La seconde partie est une intro
EE-311: Fundamentals of machine learningCe cours présente une vue générale des techniques d'apprentissage automatique, passant en revue les algorithmes, le formalisme théorique et les protocoles expérimentaux.
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 an
FIN-407: Machine learning in financeThis course aims to give an introduction to the application of machine learning to finance, focusing on the problems of portfolio optimization and hedging, as well as textual analysis. A particular fo
CS-456: Deep reinforcement learningThis course provides an overview and introduces modern methods for reinforcement learning (RL.) The course starts with the fundamentals of RL, such as Q-learning, and delves into commonly used approac
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
MGT-418: Convex optimizationThis course introduces the theory and application of modern convex optimization from an engineering perspective.
