Duration
Theory : 24h Th
Practical work : 6h Pr, 10h AUTR
Number of credits
Lecturer
Theory : Vân Anh Huynh-Thu
Practical work : Vân Anh Huynh-Thu
Coordinator
Language(s) of instruction
French 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
Theory
This course focuses on machine learning, a subfield of artificial intelligence (AI) that involves training a computer to perform a specific task based on data related to that task. The applications of machine learning are now ubiquitous, ranging from film and music recommendations to machine translation, bank fraud detection, medical image analysis and the prediction of biological phenomena.
This course aims to introduce you to the fundamental principles of the main machine learning algorithms, as well as methods for manipulating, analysing and visualising data.
The topics covered will be as follows (subject to change):
- Exploratory data analysis (graphical and non-graphical analysis)
- Standard machine learning (nearest neighbour algorithm, linear models, tree-based methods, performance estimation)
- Deep learning (artificial neural networks, generative AI)
- Interpretability in AI and explainable AI
- Unsupervised learning (clustering, dimensionality reduction)
Practical work
This course focuses on machine learning, a subfield of artificial intelligence (AI) that involves training a computer to perform a specific task based on data related to that task. The applications of machine learning are now ubiquitous, ranging from film and music recommendations to machine translation, bank fraud detection, medical image analysis and the prediction of biological phenomena.
This course aims to introduce you to the fundamental principles of the main machine learning algorithms, as well as methods for manipulating, analysing and visualising data.
The topics covered will be as follows (subject to change):
- Exploratory data analysis (graphical and non-graphical analysis)
- Standard machine learning (nearest neighbour algorithm, linear models, tree-based methods, performance estimation)
- Deep learning (artificial neural networks, generative AI)
- Interpretability in AI and explainable AI
- Unsupervised learning (clustering, dimensionality reduction)
Learning outcomes of the learning unit
Theory
At the end of the course, you will have acquired an overview of the main machine learning algorithms.
Practical work
At the end of the course, you will have acquired an overview of the main machine learning algorithms. You will also be able to apply them to real data and rigorously evaluate their performance.
Prerequisite knowledge and skills
Theory
There is no prerequisite.
Practical work
The course includes a practical project that requires basic programming knowledge.
Planned learning activities and teaching methods
Theory
The course consists of theoretical lessons presenting the fundamental principles of machine learning.
Practical work
The course will include theoretical lessons presenting the fundamental principles of machine learning. These lessons will be supplemented by practical tutorial sessions and a project, aimed at putting the concepts covered into practice.
If possible, one or more seminars will be organised, during which the speaker will present a practical application of artificial intelligence in your discipline.
Mode of delivery (face to face, distance learning, hybrid learning)
Theory
Face-to-face course
Further information:
The course is given during the first semester.
Practical work
Face-to-face course
Further information:
The course is given during the first semester.
Theoretical lectures and tutorial sessions are face-to-face. The project is carried out remotely.
Course materials and recommended or required readings
Theory
Platform(s) used for course materials:
- eCampus
Practical work
Platform(s) used for course materials:
- eCampus
Theory
Exam(s) in session
Any session
- In-person
oral exam
Further information:
The purpose of the oral examination will be to assess your understanding of the concepts covered in the lectures. You will be required to present one or several parts of the course and answer questions covering the entire course material.
Practical work
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 the code.
The projet is mandatory to access the exam.
2. In session oral exam
The purpose of the oral examination will be to assess your understanding of the concepts covered in the lectures. You will be required to present one or several parts of the course and answer questions covering the entire course material and the project you have completed.
Work placement(s)
Organisational remarks and main changes to the course
Theory
All course information will be posted on eCampus.
Practical work
All course information will be posted on eCampus.
Contacts
Theory
Professor : Vân Anh Huynh-Thu.
Email : vahuynh@uliege.be
Office : 1.84b, B28 (Montefiore Institute, Sart-Tilman)
Practical work
Professor : Vân Anh Huynh-Thu.
Email : vahuynh@uliege.be
Office : 1.84b, B28 (Montefiore Institute, Sart-Tilman)