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.

With the explosion and profusion of data available in a large number of fields, new collection and analysis techniques are having a profound effect on all sectors of economic life, but also on certain research sectors. The course offers an introduction to the best methodological tools for understanding and exploiting data, particularly massive data (Big Data). An immersion in the world of data through an intensive course on the fundamentals of data science.

Artificial Intelligence Internals (168 heures) Montée en compétences techniques en IA : Gestion des masses des données et apprentissage automatique  (Data Integration and Exploration, Big Data Processing, Optimization topics  for AI, Machine Learning and Data Analytics, Sequential Decision Making in AI,  AI certification, robustness and dependability).