MGT-581: Introduction to econometricsThe course provides an introduction to econometrics for economics and financial applications. The objective is to learn how to make valid (i.e., causal) inference from economic and social data.
ME-301: Measurement techniquesTheoretical and practical course on experimental techniques for observation and measurement of physical variables such as force, strain, temperature, flow velocity, structural deformation and vibratio
AR-597(a): Superstudio ASous le titre "DOMESTICATED FOODSCAPES", Superstudio explore des perspectives oubliées et des approches proactives pour repositionner l'architecture dans le contexte de l'alimentation.
CS-444: Virtual realityThe goal of VR is to embed the users in a potentially complex virtual environment while ensuring that they are able to react as if this environment were real. The course provides a human perception-ac
DH-411: Design research for digital innovationHow can we turn digital technologies and data into meaningful user experiences? How can we face societal issues raised by digital evolution? This course proposes an immersion in design research, user
CS-401: Applied data analysisThis course teaches the basic techniques, methodologies, and practical skills required to draw meaningful insights from a variety of data, with the help of the most acclaimed software tools in the dat
MGT-416: Causal inferenceStudents will learn the core concepts and techniques of network analysis with emphasis on causal inference. Theory and
application will be balanced, with students working directly with network data th
NX-423: Translational neuroengineeringThis course integrates knowledge in basic, systems, clinical and computational neuroscience, and engineering with the goal of translating this integrated knowledge into the development of novel method
HUM-402: Experimental history of science IThe course allows students to learn by doing about the history of science, and the role played by experimentation, technical skills or material objects in the production of knowledge. Students will ex
MATH-336: Randomization and causationThis course covers formal frameworks for causal inference. We focus on experimental designs, definitions of causal models, interpretation of causal parameters and estimation of causal effects.
