MATH-432: Probability theoryThe course is based on Durrett's text book
Probability: Theory and Examples.
It takes the measure theory approach to probability theory, wherein expectations are simply abstract integrals.
EE-559: Deep learningThis course explores how to design reliable discriminative and generative neural networks, the ethics of data acquisition and model deployment, as well as modern multi-modal models.
MATH-487: Topics in stochastic analysisThis course offers an introduction to topics in stochastic analysis, oriented about theory of multi-scale stochastic dynamics. We shall learn the fundamental ideas, relevant techniques, and in general
MATH-489: Number theory II.c - CryptographyThe goal of the course is to introduce basic notions from public key cryptography (PKC) as well as basic number-theoretic methods and algorithms for cryptanalysis of protocols and schemes based on PKC
PHYS-231: Data analysis for PhysicsCe cours présentera les bases de l'analyse des données et de l'apprentissage à partir des données, l'estimation des erreurs et la stochasticité en physique. Les concepts seront introduits théoriquemen
MATH-519: Topics in high-dimensional probabilityThis is a theoretical course about probability in high dimensions. We will look at some mathematical phenomena appearing as the number of random variables grows large - e.g. concentration of measure o
