Course Overview

* Introduction
* Bayesian estimators
* Priori choice
* Approximate Bayesian inference
** Deterministic approximation methods
** Stochastic approximation methods
* Case study: Bayesian inference for speech recognition

http://phelma.grenoble-inp.fr/en/studies/bayesian-methods-for-data-image-analysis-wpmtbmd7

Learning Achievement

The aim is to introduce fundamentals on Bayesian inference, and to develop applications in the framework of image and signal processing.

Competence

* Introduction
* Bayesian estimators
* Priori choice
* Approximate Bayesian inference
** Deterministic approximation methods
** Stochastic approximation methods
* Case study: Bayesian inference for speech recognition

Course prerequisites

Basic notion in both estimation and detection theory

Grading Philosophy

Written exam

Course schedule

Course type

Lecture

Online Course Requirement

Instructor

Olivier Michel, Hacheme Ayasso, Florent Chatelain

Other information

Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Please consider the following deadlines for inbound mobility to Grenoble:
- April 1st, 2020 for Full Year (September to June) and Fall Semester (September to January) intake ;
- September 1st, 2020 for Spring Semester intake (February – June).

Site for Inquiry