Education in Artificial Intelligence

Our objective is to increase the number of persons with skills in AI technologies, to enable society to fully exploit their innovation potential. To this end, we propose to create programmes that would formally qualify practitioners in AI technologies. These programmes would be open to anyone with the prerequisite abilities, including both registered students and the general population. Specifically, we propose to create a professional qualification in AI. This qualification will be transversal to existing degrees. It will be delivered by the MIAI Grenoble Alpes Institute and would certify the acquisition of both theoretical and practical AI competence and skills.

8 Master programmes already dedicated to AI:

Master Track Mention School or Faculty
MOSIG - Data Science Informatics Grenoble INP - Ensimag & UGA - IM2AG
MOSIG - Artificial Intelligence and the Web Informatics Grenoble INP - Ensimag & UGA - IM2AG
MSIAM - Fundamentals of Data Science Maths & Applications Grenoble INP - Ensimag & UGA - IM2AG
MSIAM - Large Scale Data Science Maths & Applications Grenoble INP - Ensimag & UGA - IM2AG
Statistics and Data Science Maths & Applications UGA - IM2AG & UGA - SHS
MS Big Data Mastère spécialisé Grenoble INP - Ensimag & Grenoble EM
Cognition Naturelle ou Artificielle Cognitive Sciences Grenoble INP - Phelma and UGA - SHS
SIGMA Signal and Image Processing Traitement du Signal et des Images Grenoble INP - Phelma

Current Situation in Grenoble

700 students are currently AI-educated in Grenoble through disciplinary academic programmes in AI technologies and applications. However, the aspects of AI currently taught are specific to each diploma, limiting both multidisciplinarity and the validation of AI skills individually acquired by students. This organisation also impedes the sharing of resources, especially with respect to practices of AI technologies. We aim at improving the visibility, attractivity and coherence of this offer through an integrated approach of the teaching and learning offering in AI.

Actions of MIAI

We will create the label “Professional Qualification in Artificial Intelligence”, delivered by MIAI and attesting to the acquisition of both theoretical and practical AI skills. This label will be accessible through competence blocks transversal to existing degrees. Two sub-labels will be considered: AI core and integration, corresponding to actionable knowledge in the central themes of AI (algorithms for AI, machine learning, symbolic reasoning, computer vision, robotics, natural language processing, multi-agent systems, etc.) and AI practices and application, corresponding to actionable knowledge in the use of AI for disciplinary domains as health, environment, energy, mobility, industry 4.0, management, etc. The demand is high in these latter fields and we expect an important increase in the number of students willing to obtain an AI label in addition to their domain-specific diploma.

Requirements for the MIAI labels

The AI labels will be articulated with the accredited diplomas and include practical AI sessions. At the Bachelor and Master levels, obtention of an AI label will depend on the successful validation of a certain number of courses (measured in European Credit Transfer Scale - ECTS). For PhD students, the requirement corresponds to the (estimated) amount of time devoted to research and training in AI related fields. 
 
To enhance actionable knowledge, we will require practical work in AI. Such practical work can be performed either in existing courses or within hackatons defined with industrial partners and for which MIAI will provide data and testbed environments. MIAI will also manage “Innovation projects for AI” for which industrial partners or students propose projects focusing on short-term prototypes. Promising projects will be presented at the yearly MIAI Days.

Formats and new modules to be developed

Both professional labels will be accessible in three modalities:

  • Short-cycle courses in which classes, coordinated by MIAI, are given in a limited period, specifically addressing individuals from the socio-economic world. Transversal to the existing accredited diplomas, those AI courses are opened to students from any discipline;
  • On-demand courses, also coordinated by MIAI, to answer the educational needs of companies or institutions in AI (within a life-long learning perspective);
  • Long-cycle courses articulated with the existing accredited diplomas. Disciplinary programs will increase the integration of AI in their core to enable students to obtain a professional qualification in AI. In each diploma, courses with AI-content will be identified with their respective contribution to the AI qualification (in hours of work or ECTS).

We will also create several new tracks and programmes in AI:

  • Bachelor programme dedicated to AI;
  • Master track, within the existing Master on Computer Science Applied to Business Administration, dedicated to Data Management and Business Intelligence;
  • Master programme on Business and Artificial Intelligence (MBAI), developed jointly by Grenoble School of Management and the Institute of Business Administration of Univ. Grenoble Alpes;
  • Professional Master track, co-designed with our industrial partners within the existing Computer Science and Applied Mathematics Masters, on practical AI. During their last year, selected students will follow courses for one third of their time and will work within a company for the other two thirds;
  • Lastly, several modules will be created in existing programmes, in particular in application domains of AI as health, environment, industry 4.0., business and administration, law, humanities and social sciences.

Targets

All courses within either long- or short-cycle programmes will be open to individuals from outside the university community, subject to demonstration of prerequisites abilities and possible limitations on the number of students. We estimate that with the development of the AI labels and the creation of new programmes and tracks, the number of persons graduating in core AI (label AI core and integration) will increase from 300 in 2018 to 500 in 2022, and that the number of persons graduating in application domains (label AI practices and application) will increase from 400 in 2018 to 900 in 2022. We thus expect to double the number of persons educated in AI at all levels, from 700 in 2018 to 1400 in 2022. In addition, we expect that roughly 40 professionals will follow on-demand courses each year.