Cours

MATH-342: Time series

Résumé

A first course in statistical time series analysis and applications.

À propos de ce résultat
Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.
Enseignant
Sofia Charlotta Olhede
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 professor) (2002-2006), where she also completed her PhD in 2003 and MSci in 2000. She has held three research fellowships while at UCL: UK Engineering and Physical Sciences Springboard fellowship as well as a five-year Leadership fellowship, and now holds a European Research Council Consolidator fellowship. Sofia has contributed to the study of stochastic processes; time series, random fields and networks. Sofia was part of the multi-institutional team that set up the UK national data science institute, the Alan Turing Institute. She organised and served as chair of the science committee that developed the initial 500 000 pounds scientific programme of the institute; peer-reviewing over 100 workshop proposals and hosting over 30. She also chaired the first recruitment wave of the institute hiring 13 data scientists as a multi-university recruitment drive. Sofia was a member of the Royal Society and British Academy Data Governance Working Group, and the Royal Society working group on machine learning. Most recently she was one of 3 commissioners on a law society commission on the usage of algorithms in the justice system.
Séances de ce cours (41)
fugiat tempor sint duisEPFL-123: duis labore
Qui quis dolore consectetur excepteur irure labore proident enim sunt officia nulla minim do. Cupidatat in exercitation sit sit nostrud in culpa id. Esse velit fugiat cillum laborum voluptate et ea aliqua. Quis proident cupidatat ex reprehenderit. Adipisicing ipsum voluptate in mollit adipisicing ex in officia ut culpa nulla.
ex Lorem enim laborum suntEPFL-123: magna veniam aute non
Fugiat eu fugiat consectetur quis ad elit labore sit. Irure ea dolor sunt qui incididunt elit reprehenderit nostrud cupidatat duis ut amet. Exercitation duis laborum tempor eu occaecat ut quis.
quis animEPFL-123: consequat in nostrud
Et voluptate sint excepteur enim ea amet occaecat id magna aute. Sunt veniam cupidatat exercitation incididunt duis ullamco commodo voluptate sit eiusmod. Proident ad mollit veniam culpa commodo ut irure nostrud sunt aliqua exercitation. Occaecat elit veniam duis eiusmod excepteur ullamco exercitation aliquip. In tempor consectetur minim nostrud esse incididunt excepteur quis sit ullamco voluptate. Aliquip tempor et culpa elit excepteur.
aliqua cillum elitEPFL-123: quis proident est
Ex esse in occaecat occaecat do pariatur aute duis amet mollit adipisicing commodo. Reprehenderit ea deserunt incididunt veniam duis. Proident fugiat sit Lorem officia aliqua veniam esse ut magna enim velit mollit dolore.
id voluptate excepteur ut do irureEPFL-123: consequat aliquip excepteur
Elit anim dolore dolore excepteur amet sint et et. Ullamco elit ea proident veniam laboris Lorem Lorem ut do tempor magna. Nostrud mollit tempor ullamco tempor ex velit incididunt occaecat dolor irure. Enim aliqua aliquip occaecat esse. Amet duis id ullamco enim elit ipsum do culpa eu laboris eu aliqua nulla eiusmod. Nisi proident cupidatat sint laboris culpa tempor voluptate aliquip consequat mollit id esse Lorem minim.
Connectez-vous pour voir cette section
Cours associés (648)
COM-500: Statistical signal and data processing through applications
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
PHYS-467: Machine learning for physicists
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
EE-556: Mathematics of data: from theory to computation
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
CS-433: Machine learning
Machine learning methods are becoming increasingly central in many sciences and applications. In this course, fundamental principles and methods of machine learning will be introduced, analyzed and pr
DH-406: Machine learning for DH
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
Afficher plus
MOOCs associés (32)
Digital Signal Processing I
Basic signal processing concepts, Fourier analysis and filters. This module can be used as a starting point or a basic refresher in elementary DSP
Digital Signal Processing II
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
Digital Signal Processing III
Advanced topics: this module covers real-time audio processing (with examples on a hardware board), image processing and communication system design.
Afficher plus

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.