COM-300: Stochastic models in communicationL'objectif de ce cours est la maitrise des outils des processus stochastiques utiles pour un ingénieur travaillant dans les domaines des systèmes de communication, de la science des données et de l'i
MGT-424: Advanced data driven business analyticsThis course aims to provide graduate students a grounding in the methods, theory, mathematics and algorithms needed to apply machine learning techniques to in business analytics domain. The course cov
MATH-512: Optimization on manifoldsWe develop, analyze and implement numerical algorithms to solve optimization problems of the form min f(x) where x is a point on a smooth manifold. To this end, we first study differential and Riemann
MATH-600: Optimization and simulationMaster state-of-the art methods in optimization with heuristics and simulation.
Work involves:
- reading the material beforehand
- class hours to discuss the material and solve problems
- homework
MATH-352: Causal thinkingThis course will give a unified presentation of modern methods for causal inference. We focus on concepts, and we will present examples and ideas from various scientific disciplines, including medicin
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-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.
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
