Statistical Signal Processing

Information

Teacher coordinatorRomain Hérault
Teacher(s)Romain Hérault
LanguageEnglish
Credits4.5
TeachingLectures : 21h Exercises : 21h
OptionData science
Web sitehttps://moodle.insa-rouen.fr/enrol/index.php?id=942

Aim and objective

In this lecture, signal processing problems will be addressed in a machine learning point of view.

Outcome learning

  • INSA reference data :
    • Obtenir une description statistique d'un ensemble de données [3P]
    • Filtrer et modéliser des signaux [3P]
  • CNISF reference data :
    • H30T [3P]
    • J40K [1P]
    • J10P [3P]
1 - Notion, 2 - Concept, 3 - Application, I - fully, P - incomplete

Course description

  • Random Signals
  • Stochastic Linear Systems
  • Bayesian filtering and smoothing
  • Kalman filtering and smoothing
  • Particle filtering and smoothing
  • Hidden Markov Model
  • Change point detection

Prerequisites

  • Statistics and Numerical Analysis,
  • Signal Processing,
  • Numpy, Matlab/Octave or Scilab programmation

Bibliography

Assessment

  • Final Exam: 50%
  • In-class evaluation and project: 50%