MATH-642: Artificial LifeWe will give an overview of the field of Artificial Life (Alife). We study questions such as emergence of complexity, self-reproduction, evolution, both through concrete models and through mathematica
CS-459: Foundations of probabilistic proofsProbabilistic proof systems (eg PCPs and IPs) have had a tremendous impact on theoretical computer science, as well as on real-world secure systems. They underlie delegation of computation protocols a
CS-250: Algorithms IThe students learn the theory and practice of basic concepts and techniques in algorithms. The course covers mathematical induction, techniques for analyzing algorithms, elementary data structures, ma
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
MATH-522: Empirical processesFrom prototypical examples of estimators used by statisticians, to more complex nonparametric models, methods and theorems will be taught to study their (non)asymptotic behavior, when defined as mappi
EE-566: Adaptation and learningIn this course, students learn to design and master algorithms and core concepts related to inference and learning from data and the foundations of adaptation and learning theories with applications.
AR-455: Urbanism and TerritoriesCe cours adopte une démarche de déconstruction de la complexité urbaine pour aborder l'urbanisme et les territoires, en s'appuyant sur une méthode systémique.
