Duration
24h Th, 50h Proj.
Number of credits
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the second semester
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
Artificial intelligence models are increasingly applied across domains, including domains (such as healthcare, finance or law) where the outputs generated by the models may affect individuals. It is therefore important to ensure that these models are not only accurate but also that the relevant stakeholders are able to understand their behaviour. Such an understanding helps to determine whether one can rely on the outputs of the IA models and what their limitations are. In this context, this course aims to introduce you to the existing methodologies in the emerging field of explainable artificial intelligence (or XAI for short).
The topics covered will be as follows (subject to change):
- Inherently interpretable models
- Feature attribution methods and saliency maps
- Counterfactual explanations
- Concept-based explanations
- Explanations with and for large language models
- Evaluation of explanations
- Connections between XAI and fairness
Learning outcomes of the learning unit
At the end of the course, you will have acquired an overview of the state of the art in the field of explainable IA.
Prerequisite knowledge and skills
ELEN0062 (Introduction to Machine Learning) is a corequisite. The course will cover materials that assume a good prior knowledge of the foundations covered in ELEN0062.
Planned learning activities and teaching methods
The course will consist of lectures introducing the key methodological concepts and the state-of-the-art approaches in the field of XAI. The lectures will be complemented by a practical project, in which you will implement an existing or a new XAI method and experiment with it on real datasets.
Mode of delivery (face to face, distance learning, hybrid learning)
Face-to-face course
Further information:
The course is given during the second semester.
Theoretical lectures are face-to-face. The project is carried out remotely.
Course materials and recommended or required readings
Platform(s) used for course materials:
- eCampus
Exam(s) in session
Any session
- In-person
oral exam
Written work / report
Continuous assessment
Further information:
You will be evaluated in two ways:
1. Continuous assessment
A practical project is to be carried out during the semester and will be assessed on the basis of a written report and an oral presentation.
2. In session oral exam
The oral exam will consist of a presentation of a scientific article and the theoretical concepts associated with it.
Work placement(s)
Organisational remarks and main changes to the course
The course is taught in English.
All course information will be posted on eCampus.
Contacts
Professor : Vân Anh Huynh-Thu.
Email : vahuynh@uliege.be
Office : 1.84b, B28 (Montefiore Institute, Sart-Tilman)