This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Laboris consectetur deserunt aliquip proident amet officia consectetur esse eiusmod mollit dolor irure culpa ea. Id et anim consectetur do irure ullamco ullamco pariatur et ut deserunt esse. Anim elit id quis nisi. Ullamco est qui enim voluptate et adipisicing quis dolore nostrud enim aute incididunt et.
Commodo laboris irure dolore in. Proident adipisicing mollit sint minim minim nulla nulla et Lorem nisi aliquip aute excepteur. Irure sit et duis consectetur esse aliquip laboris. Nulla proident qui aliqua consequat excepteur veniam dolore cillum. Laboris mollit elit in non velit. Lorem aute exercitation officia anim est velit velit. Sunt velit culpa aute laborum adipisicing laborum incididunt mollit labore amet magna.
Mollit occaecat amet aute ad exercitation commodo. Veniam Lorem magna adipisicing velit voluptate velit labore enim occaecat labore officia adipisicing consectetur minim. Irure ea aute irure voluptate magna minim ea laboris. Voluptate ipsum exercitation nostrud culpa eu culpa aliqua ut duis tempor exercitation dolor enim sunt.
Sofia Olhede is a professor of Statistics at EPFL in Switzerland. She joined UCL prior to this in 2007, before which she was a senior lecturer of statistics (associate professor) at Imperial College London (2006-2007), a lecturer of statistics (assistant p ...
Adaptive signal processing, A/D and D/A. This module provides the basic
tools for adaptive filtering and a solid mathematical framework for sampling and
quantization
This course aims to introduce the basic principles of machine learning in the context of the digital humanities. We will cover both supervised and unsupervised learning techniques, and study and imple
Machine learning and data analysis are becoming increasingly central in sciences including physics. In this course, fundamental principles and methods of machine learning will be introduced and practi
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
Building up on the basic concepts of sampling, filtering and Fourier transforms, we address stochastic modeling, spectral analysis, estimation and prediction, classification, and adaptive filtering, w
This course provides an overview of key advances in continuous optimization and statistical analysis for machine learning. We review recent learning formulations and models as well as their guarantees