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Cours associés (11)
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This course will provide an introduction to model category theory, which is an abstract framework for generalizing homotopy theory beyond topological spaces and continuous maps. We will study numerous
MATH-688: Reading group in applied topology I
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MATH-497: Topology IV.b - homotopy theory
We propose an introduction to homotopy theory for topological spaces. We define higher homotopy groups and relate them to homology groups. We introduce (co)fibration sequences, loop spaces, and suspen
MATH-211: Algebra II - groups
This course deals with group theory, with particular emphasis on group actions and notions of category theory.
MATH-410: Riemann surfaces
This course is an introduction to the theory of Riemann surfaces. Riemann surfaces naturally appear is mathematics in many different ways: as a result of analytic continuation, as quotients of complex
DH-406: Machine learning for DH
This course aims to introduce the basic principles of machine learning in the context of the digital humanities. We will cover both supervised and unsupervised learning techniques, and study and imple
ME-104: Introduction to structural mechanics
The student will acquire the basis for the analysis of static structures and deformation of simple structural elements. The focus is given to problem-solving skills in the context of engineering desig
ENV-370: Environmental system analysis and assessment
Les enjeux environnementaux doivent être abordés de façon systémique. L'Analyse du Cycle de Vie (ACV) et l'Analyse de Flux de Matière (AFM) sont des méthodes permettant d'évaluer de façon globale les
MATH-687: Algebraic models for homotopy types
ln this course we will develop algebraic and coalgebraic models for homotopy types. Among other things we will learn about Quillen's and Sullivan's model of rationâl homotopy types and about Mandell's
CS-233(a): Introduction to machine learning (BA3)
Machine learning and data analysis are becoming increasingly central in many sciences and applications. In this course, fundamental principles and methods of machine learning will be introduced, analy

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