Machine learning, artificial INtelligence and Data (MIND)

The program enables students to acquire essential theoretical knowledge as well as practical skills across all areas of data-driven artificial intelligence, knowledge production, and the implementation of intelligent services:

• databases for the collection, storage, management, and querying of large volumes of complex data,
• information retrieval and data mining for filtering, analyzing, and extracting information,
• machine learning, symbolic learning, and deep learning for training models, performance evaluation, and using foundational models,
• computational intelligence for reasoning with and utilizing knowledge extracted from data.

The MIND program prepares students for industrial careers such as designers, developers, and users of intelligent systems in sectors where skills in data processing, analysis, and valorization are essential (including roles such as Data Scientist, Data Analyst, Data Architect, and Data Engineer). These constantly evolving professions require continuous updating of scientific knowledge. To meet these demands, the program includes a strong theoretical component, enabling students to adapt to technological innovations. Simultaneously, the program provides solid preparation for scientific research and careers in R&D.
Our graduates join companies of all types (start-ups, SMEs, large corporations), with approximately 30% going on to pursue a Ph.D. in either academic or industrial settings.

Please note that almost all courses are taught in French, although all teachers speak English. The first semester of the M1 year features a core curriculum with a high level of shared coursework alongside other tracks within the computer science program. Two mandatory courses and two highly recommended courses introduce:

• the main models and languages for the storage and access of structured and semantic data (MLBDA),
• foundational tools and best practices in data science and machine learning (DALAS),
• knowledge representation and management (LRC),
• the use of probabilistic approaches for data analysis (MAPSI).

These courses are supplemented by courses from other tracks aimed at providing essential mathematical and computational tools for the program, along with additional subjects (such as linear programming, graph theory, image processing, and algorithm complexity analysis).
Starting from the second semester of M1, the program offers three specialized skill profiles—Machine Learning, Databases, and Artificial Intelligence—each delivering a set of specific competencies through a customized arrangement of course units (UE).
The course units (UE) within each profile complement those of the other two profiles, allowing students to customize their training.
In the first semester of the M2 year, students take specialized UEs that each provide a specific set of skills aligned with the different profiles (such as Deep Learning, Large Language Models, AI for Science, Big Data, Knowledge Graphs, and Interpretable AI). Students’ career paths are further shaped by their internships in the second semester of M2, which can be completed either in a research lab or a company, with a focus on research or development.
Students enrolled in the M2 MIND program who meet the necessary prerequisites can also take UEs covering advanced mathematical topics (such as convex and non-convex optimization, statistical learning, and machine learning) offered by the M2A program (http://m2a.lip6.fr).

Upon graduation, the graduate will be expected to master the following disciplinary skills:

• Understand and implement principles for managing both structured and unstructured databases.
• Create, manage, and utilize databases, ensuring their quality to guarantee reliable data access.
• Collect, prepare, analyze, and process data.
• Master digital tools and programming languages commonly used in AI.
• Select and implement machine learning and deep learning algorithms to address a given problem.
• Mathematically model an AI problem to facilitate its resolution.
• Approach a business problem to identify the needs in data science, AI, and database management.
• Implement and deploy data science methods in a business context to meet industry expectations.
• Evaluate the performance of an AI system in relation to business objectives.
• Explain an AI system.

Admission to the M1 level is primarily for students at the L3 level (or equivalent) in Computer Science or Computer Science/Mathematics. Well-motivated applications from other scientific fields may also be considered.
At the M2 level, the program can admit a few external candidates who meet the prerequisites, particularly final-year engineering students who wish to pursue a dual degree and already have a foundational background in AI.
Applicants should have a strong foundation in computer science (algorithmics and programming, databases, logic, etc.) along with fundamental knowledge in mathematics (probability, statistics, calculus, algebra).
The MIND program is not available as a work-study option.