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Artificial Intelligence and Machine Learning for Connected Systems

Code UE : USEEN6

  • Cours
  • 6 crédits

Responsable(s)

Public, conditions d’accès et prérequis

M1 courses or equivalent courses done at another institution.

Objectifs pédagogiques

The objective of the course is to study basics of machine learning and artificial intelligence algorithms used for network applications and IoT systems optimisation and acquire hands-on experience via experimental labs. The course will show how conventional ML/AI algorithms can be challenged in their performance and accuracy when running under constraints emerging in network and IoT systems environment, as for instance : execution time target, limited live and storage memory space, energy consumption and power limitations.

Mots-clés

Contenu

The course covers the following topics with half of the lessons as practical labs :
    • refresh on statistics and network optimisation
    • unsupervised machine learning
      • main algorithms, comparison, experimentation
      • time-constrained applications (traffic anomaly detection, etc)
      • memory-constrained applications (spatio-temporal mobility characterization, etc)
    • supervised machine learning and applications
      • main algorithms, comparison, experimentation
      • time-and-energy-constrained application (IP traffic classification, etc)
      • time-and-memory-constrained applications (cyber attack classification, etc)

 

Modalité d'évaluation

Evaluation of TP lab reports and of a final exam.

Cette UE apparaît dans les diplômes et certificats suivants

Contact

EPN05 - Informatique
33.1.13A, 2 rue Conté
75003 Paris
Tel :01 40 27 28 49
Mariella Annicchiarico
Voir le site

Voir le calendrier, le tarif, les conditions d'accessibilité et les modalités d'inscription dans le(s) centre(s) d'enseignement qui propose(nt) cette formation.

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