Computer Science for Artificial Intelligence

Overview

Artificial intelligence (AI) is one of today's major scientific challenges. Recent advances in statistical learning have led to significant breakthroughs in many economic and societal fields. But these advances only reveal their full potential when integrated into a larger ecosystem, which lies within the historical scientific field of "artificial intelligence".

The specialisation in Computer Science for Artificial Intelligence takes a broad approach to this disciplinary field, covering statistical learning but also game theory, logic programming, reinforcement learning, ethics, etc.

The specialisation is a computer science course, focusing on AI, algorithms, and their implementation in practice.

Contribution to sustainable development goals

SDG 3 - GOOD HEALTH AND WELL-BEING

SDG 9 - INDUSTRY, INNOVATION AND INFRASTRUCTURE

► Learn more about Centrale Nantes' commitment to the 17 sustainable development goals

Admission

International students can follow this specialisation, taught in French, via:

A double degree programme - Open to international students selected by our partner institutions. Selected students spend two years studying courses from the engineering programme at Centrale Nantes. This usually includes one year of the common-core engineering curriculum followed by one year of specialisation. Double degree students are typically accepted after successfully completing two or three years of higher education in their home institution.
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The fast-track engineering programme: Open to students with a Bachelor's or equivalent degree in science. Our fast-track programme gives international students who are qualified to bachelor level the opportunity to gain the 'diplôme d'ingénieur' in just two years.
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Course Content

2024/25 Academic Year

Autumn Semester (S7 or S9)	Spring Semester (S8 or S10)
Advanced algorithmics	Graphs and algorithms
Sustainability, ethics and computing	Probabilistic Modeling and Reinforcement Learning
Advanced programming in Python	Programming on Graphical Processor Units
Introduction to statistics and data science with Python	Logic programming
Algorithmic Game Theory	Project 2
Parallelism and Model Checking	Internship
Quality, Design and Modelling
Deep Learning
Project 1

Download syllabus

Examples of past projects and internships

Examples of projects

Programming a Poker game with AI
SVD Decomposition of very large matrices
Identification of gene sets using logic programming
Predicting the risk of kidney dysfunction in living donors
Infrastructure for bird song recognition
Predicting tidal range with machine learning
Datascience and machine learning with online games data

Examples of internships

IA-based computer vision to enhance image quality for laparoscopic surgery (CNRS)
Neural network architecture enhancement for unsupervised learning with Deep Image Prior (INSERM)
Design and development of mobile applications (BAM)
Identification of vegetation surrounding electrical grids with machine learning (ENEDIS)
Deep Learning Diffusion Models (Thalès)
Data Engineer Natural Language Processing (Amadeus)
Continuous learning of intelligent infrastructures to control road traffic (ALTEN)
Big data AI database development (TOYOTA)

After the specialisation