As AI transforms industries, this Master’s in Artificial Intelligence offers practical training in machine learning, deep learning, NLP, reinforcement learning, and advanced topics such as generative models, frugal AI, and scientific ML. With a strong focus on applications, ethics, and bias mitigation, it prepares graduates for careers in research, industry, and entrepreneurship. Please consult the program website (https://ai-master.lisn.fr/) before applying or contacting us.
As AI continues to revolutionize industries across the globe, this Master's in Artificial Intelligence provides an advanced, hands-on approach to the core areas of AI and Machine Learning, preparing students for the latest challenges in the field. The program covers a comprehensive range of topics—from statistical learning and machine learning to subfields like deep learning, natural language processing (NLP), and reinforcement learning.
By integrating state-of-the-art techniques such as generative models, frugal AI, and scientific machine learning, students are not only trained in the theoretical underpinnings of AI but also gain real-world skills applicable to research, industry, and entrepreneurship. The program’s emphasis on practical implementation, along with courses on AI ethics and bias mitigation, ensures graduates are equipped to tackle the complex, evolving landscape of modern AI.
Informations
Skills
Graduates of this program will be able to:
- Build and deploy machine learning and deep learning models across diverse domains.
- Implement advanced AI techniques, including generative models and reinforcement learning, with modern frameworks.
- Analyze and visualize complex datasets to support data-driven decisions.
- Apply principles of ethical, fair, and responsible AI in real-world contexts.
- Design AI solutions for industries such as healthcare, finance, autonomous systems, and natural language processing.
- Demonstrate critical perspective on the history and evolution of AI, linking foundational ideas to current advancements.
Objectives
- Graduates of this program will be able to:
- • Build and deploy machine learning and deep learning models across diverse domains.
- • Implement advanced AI techniques, including generative models and reinforcement learning, with modern frameworks.
- • Analyze and visualize complex datasets to support data-driven decisions.
- • Apply principles of ethical, fair, and responsible AI in real-world contexts.
- • Design AI solutions for industries such as healthcare, finance, autonomous systems, and natural language processing.
- • Demonstrate critical perspective on the history and evolution of AI, linking foundational ideas to current advancements.
Career Opportunities
Career prospects
Chargé de développement
Consultant·e
data scientist
Ingénieur de recherche
Ingénieur de recherche ou d'études
Ingénieur d'études
ingénieur développement
ingénieur.e de recherche
Further Study Opportunities
Data Scientist, Data Analyst, Ingénieur·e en Machine Learning dans des secteurs innovants (tech, finance, santé, énergie, etc.) ;
Doctorat
Doctorat en Bioinformatique
domaines de l’apprentissage statistique, de l’intelligence artificielle ou de l’analyse de données avancée
Thèse de doctorat
Fees and scholarships
The amounts may vary depending on the programme and your personal circumstances.
Admission Route
Capacity
Available Places
Target Audience and Entry Requirements
This course requires a good background in mathematics and computer science:
• Probability and statistics
• Linear algebra
• Differential and integral calculus
• Scientific programming
• Visualization of the dataApplicants should also have completed successfully the M1 of Artificial Intelligence (or equivalent):
• Know the basics of applied statistics and optimization
• Know how to manipulate big data
• Know how to differentiate and apply techniques of supervised, unsupervised, and reinforcement learning
• Know how to program predictive models with Python and master scikit-learn
• Know how to to visualize data and illustrate results with programming tools
• Know how to write a project proposal and communicate results in writing and orally
Application Period(s)
From 01/03/2026 to 01/04/2026
Supporting documents
Compulsory supporting documents
List of post-secondary school studies mentioning exclusively the year, course, institution, average grade and your grade or distinction.
Copy of passport.
Motivation letter.
Letter of recommendation or internship evaluation.
Document at your convenience.
Completed questionnaire (to download on the master's web page).
All transcripts of the years / semesters validated since the high school diploma at the date of application.
Certificate of English level (compulsory for non-English speakers).
Curriculum Vitae.
Additional supporting documents
GMAT / GRE test results.
VAP file (obligatory for all persons requesting a valuation of the assets to enter the diploma).
Supporting documents :
- Residence permit stating the country of residence of the first country
- Or receipt of request stating the country of first asylum
- Or document from the UNHCR granting refugee status
- Or receipt of refugee status request delivered in France
- Or residence permit stating the refugee status delivered in France
- Or document stating subsidiary protection in France or abroad
- Or document stating temporary protection in France or abroad.