This 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. ...
This course provides an overview of key advances in continuous optimization and statistical analysis for machine learning. We review recent learning formulations and models as well as their guarantees
The 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. ...
We 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 ...
This 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 ...
Sitting 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 ...
"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 ...