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Coordinator of the AI master program: ai.master-informatique@universite-paris-saclay.fr
2024-25: Headmaster of M1 AI: Nona Naderi (nona.naderi@lisn.fr)
2024-25: Headmaster of M2 AI: Kim Gerdes (kim.gerdes@lisn.fr)
In partnership with Marc Evrard, and Thomas Gerald (M2 internship coordination)
Alexandre Verrechia (pedagogical secretary)
Application dates for 2025: January 20 - March 24
It is advised to apply when you have all or most of your 1st semester grades (of the current year), and if possible, around mid-March.
Applications will be reviewed by batch, one of them occuring around March 15-20. (So it is useless to apply earlier than that)
In 2024, we received 1400 files for M1 (20-30 slots) and 400 for M2 (10 slots): don't bother emailing us about your particular case.
Master 1 (first year of master) 2025 dates: January 20 - March 24, via inception for international students;
and via "Trouver Mon Master" for French students (or students already in France)Master 2 (second year of master) 2025 dates: January 20 - March 24, via inception only
[M2 Only: NEW APPLICANT AUTO-EVALUATION QUESTIONNAIRE]
[For M1, you can discard this step: attach a white page if you cannot skip it]
The AI master
The AI master
The Paris-Saclay AI track of the Computer Science Master was founded in 2019 by Isabelle Guyon.
It is a very selective 2-year master program with 500-1500 applicants at the first year entry level for ~25 slots. It inherited from a previous 1-year master2 program ran by Alexandre Allauzen. It includes a rigorous program with foundational courses and more advanced growth classes in machine learning, computer vision, and natural language processing. It is taught by 17 professors, including 14 Université Paris-Saclay faculty and 3 external experts.
The AI master is a "parcours" (track) of the Université Paris-Saclay Computer Science (Informatique) master. It delivers a master diploma in computer science in 2 years, recruiting at the BAC+3 level (license or bachelor). The classes are taught in English and include:
Foundational classes (math, probability and statistics, signal processing, and optimization)
Growth classes (data science, machine learning, computer vision, and natural language processing)
Soft skills (e.g. Language, communication, entrepreneurship)
Practical work (e.g. Projects, internships, summer schools)
It provides a balance between theoretical and practical instruction..
See CLASS LIST.
Please note that starting from 2023, we are no longer offering the AI - EIT Digital track.
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