ENG-466: Distributed intelligent systemsThe goal of this course is to provide methods and tools for modeling distributed intelligent systems as well as designing
and optimizing coordination strategies. The course is a well-balanced mixture of theory and practical activities. ...
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 theory and in implementation. ...
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 prove theorems. ...
MGT-418: Convex optimizationThis course introduces the theory and application of modern convex optimization from an engineering perspective. ...
PHYS-642: Statistical physics for optimization & learningThis course covers the statistical physics approach to computer science problems, with an emphasis on heuristic & rigorous mathematical technics, ranging from graph theory and constraint satisfaction to inference to machine learning, neural networks and st ...
CS-723: Topics in Machine Learning SystemsThis course will cover the latest technologies, platforms and research contributions in the area of machine learning systems. The students
will read, review and present papers from recent venues across the systems for ML spectrum. ...
EE-568: Reinforcement learningThis course describes theory and methods for Reinforcement Learning (RL), which revolves around decision making under uncertainty. The course covers classic algorithms in RL as well as recent algorithms under the lens of contemporary optimization. ...
ChE-605: AI in chemistry and beyond: Highlights in the fieldShould have expertise in chemistry, physics or lite and material sciences. Although a very good knowledge in Al-based
algorithms is required to fully understand the technical details, a basic knowledge is sufficient to understand the potential
of these met ...
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 ...
MATH-505: HPC for numerical methods and data analysisThe objective of this course is to provide the necessary background for designing efficient parallel algorithms in scientific computing as well as in the analysis of large volumes of data. The operations considered are the most costly steps at the heart of ...
EE-712: Advanced microwaves for wireless communicationsThis course is intended for doctoral students using microwaves and microwave equipment during their PhD. It starts with a reminder on microwave circuit theory and continues with the main issues linked to microwaves measurement and equipment. The course i ...
CH-332: Medicinal chemistrySitting at the crossroad of organic chemistry and medicine, this course outlines how an initial hit compound transitions into a lead candidate, and ultimately a drug, in the modern drug discovery world.
Note: the course is held only during 2nd half of spr ...
ME-416: Fundamentals of computer aided manufacturingStudents will be exposed to basic computer-aided manufacturing (CAM) modeling concepts, basic mathematical simulation, verification and optimization algorithms and methodologies and their applications. The students will practice their knowledge by doing pr ...
AR-301(d): Studio BA5 (Fröhlich M. & A.)"Tackle the type" deals with processes of development and redesign of architectural typologies, and addresses the theme critically under todayâs contemporary condition. Based on the analysis of a specific building typology, independent projects are to be ...
BIOENG-450: In silico neuroscience"In silico Neuroscience" introduces students to a synthesis of modern neuroscience and state-of-the-art data management, modelling and computing technologies. ...
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. ...
CS-605: Computational and statistical learning theoryStatistical learning theory for supervised learning and generalization in PAC and online models (VC theory, MDL/SRM, covering numbers, Radamacher Averages, boosting, compression, stability and connection with strong convexity in Banch spaces); Computationa ...
