Cours associés (31)
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
MATH-561: Spectral theory
This course is an introduction to the spectral theory of linear operators acting in Hilbert spaces. The main goal is the spectral decomposition of unbounded selfadjoint operators. We will also give el
COM-514: Mathematical foundations of signal processing
A theoretical and computational framework for signal sampling and approximation is presented from an intuitive geometric point of view. This lecture covers both mathematical and practical aspects of
MSE-487: Mathematical methods for materials science
The aim of the course is to review mathematical concepts learned during the bachelor cycle and apply them to concrete problems commonly found in Engineering, and Materials Science in particular.
COM-303: Signal processing for communications
Students learn digital signal processing theory, including discrete time, Fourier analysis, filter design, adaptive filtering, sampling, interpolation and quantization; they are introduced to image pr
MATH-412: Statistical machine learning
A course on statistical machine learning for supervised and unsupervised learning
MATH-520: Topics in machine learning
Mathematical analysis of modern supervised machine learning techniques, from linear methods to artificial neural networks.
PHYS-757: Axiomatic Quantum Field Theory
Presentation of Wightman's axiomatic framework to QFT as well as to the necessary mathematical objects to their understanding (Hilbert analysis, distributions, group representations,...). Proofs of
MATH-205: Analysis IV - Lebesgue measure, Fourier analysis
Learn the basis of Lebesgue integration and Fourier analysis
MATH-451: Numerical approximation of PDEs
The course is about the derivation, theoretical analysis and implementation of the finite element method for the numerical approximation of partial differential equations in one and two space dimens

Graph Chatbot

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.