25h Th, 10h Pr, 45h Proj.
Number of credits
|Master in data science (120 ECTS)||5 crédits|
|Master of science in computer science and engineering (120 ECTS)||5 crédits|
|Master in data science and engineering (120 ECTS)||5 crédits|
|Master in computer science (120 ECTS)||5 crédits|
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 a perspective both on the artificial intelligence research goals and on the techniques developed over the years in order to build intelligent agents. It 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.
Topics to be covered:
- The overall goal and history of AI
- Solving problems with search
- Constraint satisfaction problems
- Probabilistic reasoning
- Artificial General Intelligence
- Philosophical foundations and future of AI
Many of the specialized parts (e.g. first-order logic, machine learning, optimization and control, games, computer vision, robotics) treated in the reference textbook are already covered at a deeper level in companion courses offered in our programs. Therefore, the present course will not address these topics in too much details. Rather, they will be 'discussed' by providing links with the other courses of the curriculum covering them more in details.
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 and programming tutorials.
- Programming projects whose goals will be to implement algorithms for an intelligent agent operating in a game (e.g., 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
- Oral exam (50%)
- 2 programming projects (15%+35%)
Projects are mandatory for presenting the exam.
The website for the course is https://github.com/glouppe/info8006-introduction-to-ai