A natural evolution of the projects carried out in recent years by data science and big data, what is most often referred to as artificial intelligence goes further both in terms of technological requirements and in the broad spectrum of scientific fields concerned: robotics, human-machine interaction, language processing, etc.

Big Data is a strategic priority for businesses of all sizes and sectors. It is a revolution that is bringing sweeping changes to companies and is seen as an essential tool to create value.

Télécom Paris Tech leads three teaching and research chairs related to Big Data, offers a wide range of initial and continuing training programs, and promotes innovation through its incubator, Télécom Paris Novation Center. This brings together approximately fifty research professors, fifty PhD students and about one hundred graduates per year.

The objectives of this training are to :

- To train experts in human-centred AI (HAI), capable of taking responsibility for an activity requiring motion capture, artificial learning and gesture-based interaction.

- To train project managers capable of designing, building and operating interactive systems or intelligent workspaces by enhancing the sensory-motor and cognitive capabilities of the user through a comprehensive knowledge of the business sector and the technology watch.

- Promote interdisciplinary engagement in AI.

The convergence of the fields of High Performance Computing (HPC) and massive data processing (HPDA) is now allowing the creation of intelligent systems that the professional world is taming to invent breakthrough solutions. This convergence requires the development of new interdisciplinary skills with a solid foundation in both mathematics and computer science.

The current training offer only partially covers this field of expertise, while the need for skills is constantly increasing.

The rapid growth of the internet has led to vast amounts of data being collected. As analyzing this data requires tremendous computing power, high-performance data analytics employs various parallelization techniques to reduce the execution time. These techniques can be applied to various domains such as high-performance computing, artificial intelligence and big data.

The two-year Data Artificial Intelligence Master’s program covers artificial intelligence (AI) and large-scale data management. Students will acquire the basics of machine learning, logic, big data systems, and databases, before diving into applications in advanced machine learning, symbolic AI, swarm intelligence, natural language processing, visual computing, and robotics.

The second-year Computer Science for Networks (CSN) Master’s program enables students to understand, analyze and improve communication networks, as well as develop and define software for next-generation networks. It provides techniques and tools to tackle current questions through the in-depth study of computer science and complex networks. Students will also learn to master recent approaches based on advanced software engineering.

Parallel and distributed systems are ubiquitous in many applications in our daily life including AI, online games, social networks, web services and healthcare simulations. These systems distribute computation over many computing units because they have to sustain massive workloads that cannot fit into a single computer. Designing efficient, easy-to-maintain and correct parallel and distributed systems is challenging – a challenge inherent to the complexity of managing multiple machines, many users and very large data sets.