25h Th, 10h Pr, 45h Proj.
Number of credits
Language(s) of instruction
Organisation and examination
Teaching in the first semester, review in January
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
The course aims at giving an introduction and a overview both on artificial intelligence research and on the techniques developed over the years in order to build intelligent agents.
Lectures will be based on several chapters of the textbook "Artificial Intelligence: A modern approach" (S. Russel and P. Norvig) used worldwide since 1995 in order to teach essentials of AI. Some recent developments not included in this textbook will also by covered.
Topics to be covered:
- 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
- Artificial General Intelligence and beyond
Learning outcomes of the learning unit
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.
Prerequisite knowledge and skills
Programming experience. Probability and statistics.
Planned learning activities and teaching methods
- Theoretical lectures
- Exercise sessions
- Reading assignment
- Programming projects (e.g., implement algorithms for an intelligent agent operating in a game, such as Pacman)
Mode of delivery (face-to-face ; distance-learning)
Lectures will taught face-to-face. Projects will be carried out remotely.
Recommended or required readings
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.
Assessment methods and criteria
- Exam (60%)
- Reading assignment (10%)
- Programming projects (30%)
The website for the course is https://github.com/glouppe/info8006-introduction-to-ai
- Teacher: Prof. Gilles Louppe (email@example.com)
- Assistants: Samy Aittahar (firstname.lastname@example.org), Antoine Wehenkel (email@example.com)