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ENV-501: Material flow analysis and resource management

This course provides the basis to understand material and energy production and consumption processes. Students learn how to develop a material flow analysis and apply it to resource management cases.

CS-307: Introduction to multiprocessor architecture

Multiprocessors are a core component in all types of computing infrastructure, from phones to datacenters. This course will build on the prerequisites of processor design and concurrency to introduce

ME-373: Finite element modelling and simulation

L'objectif de ce cours est d'apprendre à réaliser de manière rigoureuse et critique des analyses par éléments finis de problèmes concrets en mécanique des solides à l'aide d'un logiciel CAE moderne.

CS-421: Machine learning for behavioral data

Computer environments such as educational games, interactive simulations, and web services provide large amounts of data, which can be analyzed and serve as a basis for adaptation. This course will co

CS-448: Sublinear algorithms for big data analysis

In this course we will define rigorous mathematical models for computing on large datasets, cover main algorithmic techniques that have been developed for sublinear (e.g. faster than linear time) data

EE-452: Network machine learning

Fundamentals, methods, algorithms and applications of network machine learning and graph neural networks

PHYS-732: Plasma Diagnostics in Basic Plasma Physics Devices and Tokamaks: from Principles to Practice

The programme will allow students to learn plasma diagnostics and data processing methods of modern fusion experiments and to bridge the gap between diagnostics theory and experimental practice.

MGT-644: Conducting Qualitative Research

This workshop will expose you to a combination of readings, discussions, and hands-on exercises aimed at cementing your understanding of, and ability to conduct qualitative research - especially gathe

BIO-322: Introduction to machine learning for bioengineers

Students understand basic concepts and methods of machine learning. They can describe them in mathematical terms and can apply them to data using a high-level programming language (julia/python/R).

MATH-251(b): Numerical analysis

The students will learn key numerical techniques for solving standard mathematical problems in science and engineering. The underlying mathematical theory and properties are discussed.