Signal Processing for Artificial Intelligence study track

pictogramme cœurFor those who like

 

  • Mathematics applied to practical problems
  • Statistical learning
  • Signal processing

Objectives

Upon completion of this track, students will have a broad and operational perspective of statistical learning and signal processing. They will understand the issues surrounding data processing and big data, the methodological foundations (statistics, optimization) and techniques for processing temporal data in particular (signal processing).

In practice

The teaching prioritizes rigorous lectures and practical work in realistic conditions.

Language of instruction: English

After the track

3rd year technological innovation at Télécom Paris

Master’s-Engineering Dual Degree

  • Automation and Signal and Image Processing (Univ. Paris-Saclay)
  • Data and Artificial Intelligence (IP Paris)
  • Data Science (IP Paris)
  • Mathematics, Vision, Learning (IP Paris/Univ.
    Paris-Saclay)
  • Acoustics, signal processing and computer
    science applied to music (Sorbonne Univ.)
  • Bio-Imaging (Univ. Paris-Cité, Biomedical specification)

Professions

The track trains future engineers who will have a wide range of skills in the area of statistical learning (machine learning) and signal processing, which cover numerous fields of application: music and speech, biosignals, radio astronomy, transmission and compression of multimedia information, etc.

Testimonials

The study track offers a comprehensive education combining expertise in signal processing and artificial intelligence. The programme starts with a solid grounding in signal processing and time series analysis, before gradually moving on to more specialised courses in statistics, optimisation, machine learning and deep learning. At the end of the course, applied courses in audio processing using deep learning approaches enable the knowledge acquired to be put into practice. The strength of this programme lies in the quality of the teaching, the wealth of practical work and the expertise of the lecturers, who provide rigorous and stimulating supervision. It enables students to develop solid, versatile skills in fields that are in high demand today.
Sara Meziane, class of 2025

 

Yukun Liu

The track associates the knowledge from broad subjects, and these subjects are all explored step by step. For one example, the path of learning for machine learning is from Hilbert space to SVM, to perception, along to neural network. And this helps build solid foundations in the expertise. The track connects theory tightly with practice. Each course contains two or three practical works, and it’s always fascinating to learn the theories, implement them and witness their functionality in practical works (when they work).
Yukun Liu, class of 2022

Managers

Head: Roland Badeau
Head of international mobility: Giovanna Varni
Internship coordination: Marco Cagnazzo