Artificial Intelligence

AI has experienced a new boom in recent years, with popular fields of application such as autonomous driving, image recognition, speech recognition and game learning. The political sphere is increasingly aware of this, both from an educational and entrepreneurial point of view. The recent Villani report outlines 4 areas of innovation around artificial intelligence: transport, health, defense and the environment. AI engineering offers innovative solutions to solve IT problems when traditional solutions are inadequate.
The emergence of Deep Learning, supported by the GAFAs (Google, Amazon, Facebook, Apple) makes it possible to solve many problems through example-based learning.

These algorithms require large storage and processing capacities.
Many APIs (coupled with cloud computing technology) allow them to be manipulated in the following areas :

  • language processing: Watson API (IBM), Cloud Natural Language API (Google), Wit.AI (Facebook)
  •  image processing: Cloud Vision API (Google), Rekognition API (Amazon), CarifAI API
  •  game learning: Gym API (OpenAI), DeepMind (Google), OpenLayers API

This is an area with a bright future, strong growth and high added value. Recent predictions by the Gartner Institute confirm this:
By 2021, 30% of net growth will come from new solutions incorporating AI.
By 2020, 50% of new data transformation flows will incorporate one or more automatic learning algorithms, leading to misinterpretations of data."
By 2019, natural language generation will be a standard feature of 90% of modern BI and analysis platforms. The 20 leading web content management providers will provide natural language generation capabilities as part of their global offering.

The Artificial Intelligence option teaches all the concepts and techniques necessary for the engineer to understand the algorithms, APIs and services that enable intelligent information processing.

Business lines

Artificial intelligence engineer • Data analyst • Project manager • Research and development engineer • Data scientist

Training/Education

The teaching is organized around four axes: deep learning, natural language processing, meta-heuristic optimization, algorithmic fairness.
Complementary concepts will also be taught (reactive programming, GPU-TPU computing, image processing), as well as innovative courses that prefigure the evolution of computing (quantum computing, bioinformatics...).
The educational system will be enriched by seminars led by professionals and based on case studies.

  • Deep Learning: neural networks, convolution networks, recurrent networks, learning by reinforcement,...
  • Natural Language Processing: morphology, lexicon, syntax, semantics, grammar, word2vec, deep learning
  • Metaheuristic optimization: transport problems, scheduling problems, problems under constraints, multi-objective problems, multi-agent algorithms
  • Algorithmic fairness
  • Bioinformatics
  • Quantum Computing
  • Image processing
  •  GPU-TPU calculation
  • Reactive programming
  •  Methodology of scientific research
     
  • Case studies

 

  • Internship in a company (22 weeks minimum)
     
  • End of studies project
     

The Artificial Intelligence option is open to the professionalization contract.

Persons responsible for the option

Cergy : Souhila Arib and Julien Mercadal

Pau : Yannick Le Nir