Management and AI - 2nd year Master's degree
Syllabus
Bloc compétences technologiques
- DATA SCIENCE
- INFRASTRUCTURE
- MANAGEMENT DES BASES DE DONNEES
- ML DEEP LEARNING
- NLP
- LLM ET APPLICATIONS
Bloc Compétences Droit/Économie/Régulation/Éthique de l'IA
Bloc Compétences économie et management des IA
- STRATÉGIE DES ACTEURS DE L'IA
- ÉCONOMIE DU DIGITAL
- GOUVERNANCE DES SI ET DES IA
- SOCIOLOGIE ET POLITIQUE DES IA
- DROIT ET ÉCONOMIE DE LA RÉGULATION
- MANAGEMENT DE L'INNOVATION
Bloc Séminaires de spécialité / Cycles de conférences (3 UE à choisir)
- INTERFACE HOMME MACHINE ET UX
- IA ET CRÉATIVITÉ
- APPLICATIONS DE L'IA AUX GRH
- PUBLICITÉ DIGITALE EA L'HEURE DE L'IA
- IA DANS LES SYSTÈMES DE GESTION DE LA RELATION CLIENT
- APPLICATION DE L'IA A L'AUDIT / CONTRÔLE
- IA ET LES MÉTIERS DU DROIT
- IA ET MÉDIAS
Bloc Professionnalisation
Academic Training Year 2026 - 2027 - subject to modification
Teaching Modalities
The Master's degree comprises 476 hours of tuition, starting on September 1. The Program is based on a combination of theoretical instruction, professionalization and practical application.
Skills development is based on a pedagogical approach that alternates theory, real-life situations and case studies, thanks to a diversified approach that includes hackathons, active teaching and conference cycles. In this way, skills are applied concretely to ensure that learners acquire them properly. In terms of pedagogy, the Program places particular emphasis on the application of the knowledge and skills acquired in the classroom.
The program is divided into 4 main areas.
- Technological: Data sciences and AI.
- Governance, law and ethics.
- Economics and management of information and innovation.
- Business applications
Work-study schedule: The Program begins on September 1 for one week at the university, then 2 days at the university / 3 days at the company until mid-June.
This is an apprenticeship program, with no internship. A research paper structures and finalizes the student's academic path.
Activities:
- Translate business challenges into AI-based problems and solutions.
- Develop technical specifications based on strategic and operational requirements.
- Design, manage and develop AI projects.
- Plan the testing and validation process for AI models.
- Deploy AI solutions.
- Monitor, maintain and optimize solutions based on AI technologies.
- Collect, analyze, store, secure and prepare large volumes of data for learning, testing and inference.
- Produce results reports on AI projects.
- Collaborate with business experts and manage multidisciplinary, multi-profile teams.
- Communicate with internal and external stakeholders.
- Maintain a technology and market watch on the AI ecosystem.
- Ensure compliance with current regulatio ns and environmental, ethical and legal issues.
- Audit AI systems.