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.
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
EE-411: Fundamentals of inference and learningThis is an introductory course in the theory of statistics, inference, and machine learning, with an emphasis on theoretical understanding & practical exercises. The course will combine, and alternat
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
CS-552: Modern natural language processingNatural language processing is ubiquitous in modern intelligent technologies, serving as a foundation for language translators, virtual assistants, search engines, and many more. In this course, stude
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
CS-503: Visual intelligence : machines and mindsThe course will discuss classic material as well as recent advances in computer vision and machine learning relevant to processing visual data -- with a primary focus on embodied intelligence and visi
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
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.