Duration
25h Th, 20h Pr, 45h Proj.
Number of credits
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the first semester, review in January
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
This course offers an introduction to artificial intelligence, covering both the foundational concepts of intelligent agents and the immediate applications of AI in science and engineering.
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 the essentials of AI. The course will also integrate some of the latest developments not included in this textbook.
Topics to be covered (tentative and subject to change):
- Foundations of Artificial Intelligence
- Solving problems by searching
- Games and adversarial search
- Representing uncertain knowledge
- Inference in Bayesian networks
- Reasoning over time
- Machine Learning and neural networks
- Making decisions
- Reinforcement learning
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.
This course contributes to the learning outcomes I.1, I.2, II.1, III.1, III.2, IV.1, IV.2, V.2, VI.1, VI.2, VII.1, VII.2, VII.4, VII.5 of the BSc in engineering.
This course contributes to the learning outcomes I.1, I.2, I.3, II.1, III.1, III.2, IV.1, IV.2, IV.3, V.2, VI.1, VI.2, VII.1, VII.2, VII.4, VII.5 of the MSc in data science and engineering.
This course contributes to the learning outcomes I.1, I.2, II.1, III.1, III.2, IV.1, IV.2, IV.3, IV.8, V.2, VI.1, VI.2, VII.1, VII.2, VII.4, VII.5 of the MSc in electrical engineering.
This course contributes to the learning outcomes I.1, I.2, II.1, III.1, III.2, IV.1, IV.2, V.2, VI.1, VI.2, VII.1, VII.2, VII.4, VII.5 of the MSc in computer science and engineering.
Prerequisite knowledge and skills
Programming experience in Python. Probability and statistics.
Reminder: this is a 3rd-year course in **Computer Science**!
Planned learning activities and teaching methods
- Theoretical lectures
- Exercise sessions
- 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, hybrid learning)
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.
Exam(s) in session
Any session
- In-person
written exam
Written work / report
Additional information:
The evaluation is split into the following units:
- Written exam (60%)
- Programming projects (40%)
Work placement(s)
Organisational remarks and main changes to the course
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