D1- ARTIFICIAL INTELLIGENCE - S8

The digital world opens many challenges that concern, automated reasoning on the accessible sources of heterogeneous data, and the management of the complexity, heterogeneity and dynamics of the systems operating in this world by increasing their decision autonomy. Currently, Artificial Intelligence (AI) is one of the pillars of computer science that offers models, methods and generic technologies to tackle these challenges. AI is a domain in which sources of inspiration from different fields (biology, economics, computer science, etc.) are put in synergy. Beyond this multidisciplinary approach, this area is a source of open-mindedness for every generalist engineer. Moreover, AI is a producer of field techniques and technologies that are increasingly used in the industrial world in multiple applications (banking, logistics, games, medicine, education, robotics, defense, etc.). 
This AI course follows a pragmatic approach which faces the problem solving and the design of intelligent systems, and is partly based on the renown AIMA book from Russell & Norvig1. The AI course aims at making accessible the ideas that have emerged over the last 50 years of application of AI techniques (few formalism, pseudo-code) and show how these ideas are disseminated in current computer systems. It also exposes the multi-disciplinary nature of the disciplines at the intersection of many disciplines and techniques which are applicable in many areas. The students will understand and master the theories and technologies to develop intelligent agents, i.e. systems with (i) problem solving, (ii) reasoning and (iii) learning capabilities to perceive and act in an open and complex world. They will also learn what are the application and impact of such techniques in different domains. They will master tools and methodologies to integrate different approaches and develop intelligent systems. 
Students should have prior skills in algorithmics, procedural programming, object-oriented programming and logics. Python and Java programming languages will be used.

• UP1 – AI Society I (Overview and visions) [9h] : general course on the history of AI, seminars on ethics and AI, impacts of AI
  
• UP2 – AI Basics I (Problem Solving) [18h] : models and algorithms for navigation, search in state spaces, in graphs, for solving problems under constraints, for games
  
• UP3 – AI Basics II (Knowledge Representation and Reasoning) [21h] : models of representations and logical reasoning
  
• UP4 – AI Practice and Technos I (Simulation) [15h] : AI for the simulation of complex systems (health application)
  
• UP5 – AI Practice and Technos II (Advanced Reasoning) [15h] : AI for autonomous decision-making

Here are some examples of curricula leading to specific job careers:

• Generalist AI expert : Majeure Info + Majeur Data Science + Défi IA
• Data-oriented AI expert : Majeure Info + Majeur Data Science + Défi Big Data
• Health-oriented AI expert : Majeure BioMedicale + Majeure Gestion de production et Logistique + Défi IA
• AI developer for : Majeure Info + Majeure + Défi IA