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BIOENG-456: Controlling behavior in animals and robots

Students will acquire an integrative view on biological and artificial algorithms for controlling autonomous behaviors. Students will synthesize and apply this knowledge in oral presentations and comp

FIN-407: Machine learning in finance

This course aims to give an introduction to the application of machine learning to finance, focusing on the problems of portfolio optimization and hedging, as well as textual analysis. A particular fo

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.

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

LEARN-610: Introduction to Methods in Learning Sciences 1

This is part 1 of a two part course that provides students with knowledge and skills in collecting, processing and analyzing educational data, including different lenses to view the nature of inquiry

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.

MATH-413: Statistics for data science

Statistics lies at the foundation of data science, providing a unifying theoretical and methodological backbone for the diverse tasks enountered in this emerging field. This course rigorously develops

HUM-403: Experimental cognitive psychology I

The media frequently report on trendy studies that have been conducted in experimental cognitive psychology, and which inform the public on "human functioning" and its causes. We teach students basic

EE-452: Network machine learning

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

MATH-131: Probability and statistics

Le cours présente les notions de base de la théorie des probabilités et de l'inférence statistique. L'accent est mis sur les concepts principaux ainsi que les méthodes les plus utilisées.