Course

HUM-318: Sciences, raison et foi

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CS-401: Applied data analysis

This 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

HUM-432: How people learn: Designing Learning Tools I

The students will understand the cognitive and social factors which affect learning - particularly in science and engineering. They will be able to use social research techniques as part of the design

ENV-410: Science of climate change

The course equips students with a comprehensive scientific understanding of climate change covering a wide range of topics from physical principles, historical climate change, greenhouse gas emissions

HUM-429: Philosophy of life sciences I

Comprendre et discuter les questions centrales de la philosophie des sciences de la vie, par exemple celle du réductionnisme ou pourquoi le libre arbitre pourrait être une illusion. Transposer les pro

COM-309: Introduction to quantum information processing

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PHYS-441: Statistical physics of biomacromolecules

Introduction to the application of the notions and methods of theoretical physics to problems in biology.

HUM-471: Economic growth and sustainability I

This course examines growth from various angles: economic growth, growth in the use of resources, need for growth, limits to growth, sustainable growth, and, if time permits, population growth and gro

CS-101: Advanced information, computation, communication I

Discrete 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 a

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Le but du cours est de familiariser l'étudiant-e aux notions de base du droit et de l'éthique applicables à la recherche en LSE et à son transfert en applications, et de lui fournir les éléments essen

COM-406: Foundations of Data Science

We 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 an