1er jour - 8h30 à 17h30 :

Introduction 

Signal processing for a measurement device

            - Description of a determinist signal

            - Description of a random signal

            - Description of a measurement signal

            - Signal preprocessing and correction

            - Measurement device performances

Signal transform

            - Signal filtering

            - Signal modeling

            - Signal transform

2ème jour - 8h30 à 17h30 :

Model-driven signal processing 

            - Introduction

            - Accelerometer example

            - Basics for parameters inference

            - Introduction to Kalman filtering

            - Robust regression

            - Non-parametric inference

            - Optimization concepts

            - In a nutshell

Data-driven signal/image processing

   - Introduction

           - Basic concepts for machine learning

           - Feature based machine learning

           - Neural networks

           - Unsupervised and reinforcement learning

3ème jour - 8h30 à 12h00 :

Process Control (« Automatique »)

         - Process control and signal processing

         - Process control and control theory – Key dates

         - Closed-loop control

         - A general methodology