CS-479: Learning in neural networksArtificial Neural Networks are inspired by Biological Neural Networks. One big difference is
that optimization in Deep Learning is done with the BackProp Algorithm, whereas in biological neural
netwo
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.
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 dat
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.
DH-405: Foundations of digital humanitiesThis course gives an introduction to the fundamental concepts and methods of the Digital Humanities, both from a theoretical and applied point of view. The course introduces the Digital Humanities cir
EE-735: Online learning in gamesThis course provides an overview of recent developments in online learning, game theory, and variational inequalities and their point of intersection with a focus on algorithmic development. The prima
CS-526: Learning theoryMachine learning and data analysis are becoming increasingly central in many sciences and applications. This course concentrates on the theoretical underpinnings of machine learning.
