Course

EE-559: Deep learning

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FIN-417: Quantitative risk management

This course is an introduction to quantitative risk management that covers standard statistical methods, multivariate risk factor models, non-linear dependence structures (copula models), as well as p

CS-450: Algorithms II

A first graduate course in algorithms, this course assumes minimal background, but moves rapidly. The objective is to learn the main techniques of algorithm analysis and design, while building a reper

BIO-369: Randomness and information in biological data

Biology is becoming more and more a data science, as illustrated by the explosion of available genome sequences. This course aims to show how we can make sense of such data and harness it in order to

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.

FIN-525: Financial big data

The course introduces modern methods to acquire, clean, and analyze large quantities of financial data efficiently. The second part expands on how to apply these techniques and robust statistics to fi

MATH-234(a): Probability and statistics

Ce cours enseigne les notions élémentaires de la théorie de probabilité et de la statistique, tels que l'inférence, les tests et la régression.

PHYS-739: Conformal Field theory and Gravity

This course is an introduction to holography, the modern approach to quantum gravity.

MICRO-512: Image processing II

Study of advanced image processing; mathematical imaging. Development of image-processing software and prototyping in Jupyter Notebooks; application to real-world examples in industrial vision and bio

PHYS-216: Mathematical methods (for SPH)

Ce cours est un complément aux cours d'analyse et d'algèbre linéaire qui apporte des méthodes et des techniques mathématiques supplémentaires requises pour les cours de physique de 3e année, notamment

PHYS-642: Statistical physics for optimization & learning

This course covers the statistical physics approach to computer science problems, with an emphasis on heuristic & rigorous mathematical technics, ranging from graph theory and constraint satisfaction