M2 Data, Knowledge and Hybrid Artificial Intelligence (DKAI)

Foundations and Global Understanding

Develop a solid understanding of the theoretical and methodological foundations of data science, knowledge engineering, and hybrid artificial intelligence, covering both the symbolic and connectionist dimensions of AI.

Skills in Data Science and Hybrid Artificial Intelligence

• Analyze and model complex problems by leveraging methods from data science, symbolic AI, and hybrid AI, across diverse contexts and in connection with user needs.
• Match each problem type with appropriate approaches — machine learning, deep learning, symbolic reasoning, knowledge integration, or hybrid methods — and assess their relevance in terms of performance, explainability, and robustness.
• Identify problems that require distributed or massively parallel architectures, and deploy suitable big data tools.
• Design, manage, and operate relational databases, knowledge graphs, or unstructured data, and develop appropriate visualization and interaction interfaces for data science applications.
• Develop and deploy reliable, explainable, and trustworthy AI solutions that integrate knowledge representation, learning, and reasoning.
• Evaluate and interpret the results of complex AI/data systems according to criteria of performance, scalability, robustness, and relevance of the knowledge used.
• Implement validation and testing protocols to ensure the reliability and compliance of AI and data science solutions.
• Ensure the maintenance, evolution, and continuous adaptation of AI/data systems in line with technological advances and application needs.
• Model and reason under constraints, and extract knowledge through declarative mining and graph-based reasoning approaches.

Transversal Skills

• Mobilize advanced knowledge in machine learning, knowledge representation, and hybrid AI to develop innovative approaches.
• Develop a critical understanding of AI and data science methods, particularly regarding their interpretation limits and generalization capacity.
• Contribute to research and innovation projects in AI and data science within interdisciplinary and international contexts.
• Assume scientific and technical responsibilities to improve the practices and performance of AI/data teams.
• Communicate effectively, both orally and in writing, in French and English, to disseminate, promote, and explain the results of AI and data science projects.
• Integrate ethical, reliability, and social responsibility principles into the design and use of artificial intelligence and knowledge management systems.

The Data, Knowledge and Hybrid Artificial Intelligence (DKAI) track of the Master’s in Computer Science offers a two-year program of excellence (M1 and M2), combining data science and artificial intelligence. It brings together strong expertise in machine learning, big data, and an in-depth understanding of the different paradigms of AI—from symbolic AI to agent-based AI, including hybrid and generative AI. Throughout the curriculum, students develop their skills through numerous projects, challenges, and two internships in research laboratories or industry, preparing them for ambitious careers in both industry and research.

The DKAI track delivers advanced training over two years (M1 and M2) in data science and artificial intelligence. It combines advanced mastery of machine learning techniques, expertise in managing and analyzing massive datasets, and a deep understanding of the various paradigms of AI, from symbolic to hybrid and generative approaches. Students benefit from multiple opportunities to put their academic knowledge into practice through projects, competitions, and internships in research labs or companies.

A springboard to the careers of tomorrow

This program prepares students to tackle major challenges related to transparency, ethics, and security in the development of AI-based systems. They acquire the skills required to design cutting-edge, reliable, and explainable solutions that comply with regulatory requirements and societal expectations in sensitive sectors. The DKAI track also offers strong academic prospects, equipping students with methodological rigor and scientific expertise to pursue a PhD and contribute to state-of-the-art research in data science and artificial intelligence.

An environment of excellence

Delivered in English, supported by advanced technological resources, and embedded in a stimulating learning environment, the DKAI track trains versatile and responsible experts, ready to address the challenges of data science and artificial intelligence on both a national and international scale.