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 g
MGT-418: Convex optimizationThis course introduces the theory and application of modern convex optimization from an engineering perspective.
CS-439: Optimization for machine learningThis course teaches an overview of modern optimization methods, for applications in machine learning and data science. In particular, scalability of algorithms to large datasets will be discussed in t
MATH-207(a): Analysis IV (for SV, MT)The course studies the fundamental concepts of complex analysis with a view to their use in solving multidisciplinary problems of scientific engineering.
MICRO-515: Evolutionary roboticsThe course gives an introduction to evolutionary computation, its major algorithms, applications to optimization problems (including evolution of neural networks), and application to design and contro
MGT-483: Optimal decision makingThis course introduces the theory and applications of optimization. We develop tools and concepts of optimization and decision analysis that enable managers in manufacturing, service operations, marke
ME-428: Data-driven design & fabrication methodsThere is an increasing need for data-driven methods for automated design and fabrication of complex mechanical systems. This course covers methods for encoding the design space, optimization and sear
MATH-261: Discrete optimizationThis course is an introduction to linear and discrete optimization.
Warning: This is a mathematics course! While much of the course will be algorithmic in nature, you will still need to be able to p
MATH-329: Continuous optimizationThis course introduces students to continuous, nonlinear optimization. We study the theory of optimization with continuous variables (with full proofs), and we analyze and implement important algorith
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
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
EE-472: Smart grids technologiesLearn the technologies and methodologies used in the context of the operation of future power grids and be able to deploy/implement/test them.
ME-312: Design for XLe but du cours est de transmettre aux étudiants les concepts, les méthodes et les algorithmes de base de la conception de produit en rapport avec son cycle de vie.
ENG-421: Fundamentals in systems engineeringIntroduction to systems engineering using the classical V-model. Topics include stakeholder analysis, requirements definition, concept selection, design definition and optimization, system integration
CIVIL-447: Modelling of energy systemsLa satisfaction des besoins d'énergie à long terme pour un pays ou une region donnée nécessite de représenter l'ensemble du système énergétique en tenant compte des différentes interactions. Ce cours
ME-418: Integrated mechanical designLes objectifs du cours incluent l'approfondissement d'une approche scientifique de la conception, la pratique d'une démarche multi-thématique, la pratique de l'intégration de systèmes et l'acquisition
