Advanced Master in Artificial Intelligence

The Advanced Master in artificial intelligence, designed through a partnership between ENSTA Paris and Télécom Paris, with the participation of Télécom SudParis, combines technological requirements and complete scientific fields: robotics, human-machine interaction, language processing, etc.
The course was designed to meet the requirements of transport businesses, the automotive industry, health, banking and insurance, online services and even mass distribution.
This course is accredited by the Conférence des Grandes Écoles.

Presentation of the Advanced Master in Artificial Intelligence

The strengths of the Artificial Intelligence course

  • The possibility of designing and analysing experiments to evaluate HRI (Human-Robot Interaction) systems 
  • The use of advanced statistical learning methods of associated architectures to solve artificial intelligence issues 
  • The implementation of neural networks and deep learning methods using software libraries

Professional prospects

There are many career possibilities after the specialised master's degree in Artificial Intelligence. You can go into large businesses in the industrial and service sectors, into start-ups, or into major web companies. The skills acquired will allow you to start your career in France or abroad. 

Examples of jobs occupied by graduates of the SM in Artificial Intelligence 
Many jobs are possible, including: 

  • artificial intelligence engineer, 
  • robotics engineer, 
  • artificial intelligence researcher, 
  • designer of conversational agents, 
  • artificial intelligence specialist... 

Course programme

The course lasts for a period of 9 months, and is supplemented by a professional thesis internship of 4 to 6 months.

The teachings of the advanced master in Artificial intelligence

Fundamentals of artificial intelligence 

  • Ethics and deontology in artificial intelligence 
  • Logic and symbolic artificial intelligence 
  • Statistics 
  • Probabilistic graphical models 
  • Basics of statistical learning 

Learning and optimisation for AI 

  • Deep learning 
  • Large-scale statistical learning 
  • Reinforcement learning 
  • Learning for robotics 
  • GPGPU programming for learning 

Artificial intelligence in interaction 

  • Automatic language processing 
  • Learning for the image and recognition of objects 
  • Perception for autonomous systems 

Real case studies 

  • Personal and technical development of internship research 
  • Common thread project 
  • Scientific and industrial seminars
     

Admission requirements

Contact

Alexandre
Chapoutot
Associate professor at ENSTA Paris
Chloé
Clavel
Professor at Télécom Paris 
Ons
Jelassi Ben Atallah
EnTeacher - Head of course programmes at Télécom Paris
Deborah
Lanque
Assistant - Télécom Paris