CS-101: Advanced information, computation, communication IDiscrete mathematics is a discipline with applications to almost all areas of study. It provides a set of indispensable tools to computer science in particular. This course reviews (familiar) topics as diverse as mathematical reasoning, combinatorics, disc ...
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 and techniques that come from probability, information the ...
PHYS-423: Plasma IFollowing an introduction of the main plasma properties, the fundamental concepts of the fluid and kinetic theory of plasmas are introduced. Applications concerning laboratory, space, and astrophysical plasmas are discussed throughout the course. ...
MATH-106(f): Analysis IIÉtudier les concepts fondamentaux d'analyse et le calcul différentiel et intégral des fonctions réelles de plusieurs
variables. ...
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. ...
MGT-499: Data Science & Causal Inference for SustainabilityThis class explores key climate questions through data. Students will learn to collect, clean, and analyze data, apply causal methods using Python, and communicate insights clearly. With a focus on sustainability, the course builds skills to avoid pitfalls ...
MSE-171: MaterialsUne introduction à la science des matériaux appliquée aux matériaux de construction courants, en particulier le béton et les métaux. Description de leur fabrication, leurs comportements mécanique et thermique et leur utilisation en construction. ...
PHYS-314: Quantum physics IIThe aim of this course is to familiarize the student with the concepts, methods and consequences of quantum physics. ...
CS-457: Geometric computingThis course will cover mathematical concepts and efficient numerical methods for geometric computing. We will explore the beauty of geometry and develop algorithms to simulate and optimize 2D and 3D geometric models with an emphasis towards computational d ...
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 Riemannian geometry (with a focus dictated by computational con ...
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 approaches, like PPO and DQN. The course will introduce student ...
