Durée
25h Th, 10h Pr, 45h Proj.
Nombre de crédits
Enseignant
Langue(s) de l'unité d'enseignement
Langue anglaise
Organisation et évaluation
Enseignement au premier quadrimestre, examen en janvier
Horaire
Unités d'enseignement prérequises et corequises
Les unités prérequises ou corequises sont présentées au sein de chaque programme
Contenus de l'unité d'enseignement
The course aims at giving an introduction and an overview of artificial intelligence.
Lectures will be based on several chapters of the textbook "Artificial Intelligence: A modern approach" (S. Russel and P. Norvig) used worldwide since 1995 for teaching essentials of AI. Some recent developments not included in this textbook will also by covered.
Topics to be covered (tentative and subject to change):
- Foundations of Artificial Intelligence
- Solving problems by searching
- Constraint satisfaction problems
- Games and adversarial search
- Representing uncertain knowledge
- Inference in Bayesian networks
- Reasoning over time
- Making decisions
- Learning
- Communication
- Artificial General Intelligence and beyond
Acquis d'apprentissage (objectifs d'apprentissage) de l'unité d'enseignement
At the end of the course, the student will have a general overview of the broad field of artificial intelligence. He/she will have studied well-established algorithms for intelligent agents (both in theory and in practice), and will also have become familiar with some of the many open questions and challenges of the field.
Savoirs et compétences prérequis
Programming experience in Python. Probability and statistics.
Reminder: this is a (3rd year) Computer Science course!
Activités d'apprentissage prévues et méthodes d'enseignement
- Theoretical lectures
- Exercise sessions
- Reading assignment
- Programming projects (e.g., implement algorithms for an intelligent agent operating in a game, such as Pacman)
Mode d'enseignement (présentiel ; enseignement à distance)
Lectures will taught face-to-face. Projects will be carried out remotely.
Lectures recommandées ou obligatoires et notes de cours
Slides will be made publicly available on GitHub during the semester.
The course will be based on "Artificial Intelligence: A modern approach", Stuart Russell, Peter Norvig, Third Edition, 2010. This book is highly recommended.
Modalités d'évaluation et critères
- Exam (60%)
- Programming projects (40%)
Stage(s)
Remarques organisationnelles
The website for the course is https://github.com/glouppe/info8006-introduction-to-ai
Contacts
- Teacher: Prof. Gilles Louppe (g.louppe@uliege.be)
- Assistants: info8006@montefiore.ulg.ac.be
Adaptation des engagements pédagogiques suite à la pandémie de COVID-19 pour la session de mai-juin
Méthodes d'apprentissage mises en œuvre : enseignement à distance
Matière de l'évaluation
Méthodes d'évaluation
Contact
Adaptation des engagements pédagogiques suite à la pandémie de COVID-19 pour la session août-sept
Matière de l'évaluation
Same as for January 2020.
Méthodes d'évaluation (et plateforme utilisée)
The exam for August-September 2020 is an open book written evaluation.
The exam will be carried out remotely via eCampus. The questionnaire will be made available on the dates and times set in the examination schedule. Students will answer in writing and submit their scanned copies via eCampus within the set deadlines.
Contact(s)
g.louppe@uliege.be