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