Introduction to data science and artificial intelligence

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)

Introduction à l'intelligence artificielle pour l'architecture

This course introduces Machine Learning, a field of artificial intelligence (AI) that enables computers to learn how to perform specific tasks from data. It explores its fundamental principles and the challenges and opportunities it presents for architectural practice.

In conjunction with INFO3004 Partim 1, the course aims to introduce you to the fundamental concepts of AI and to develop a reflective and critical perspective on the use of these technologies in architecture.

Theory

At the end of the course, you will have acquired an overview of the main machine learning algorithms. 

Introduction à l'intelligence artificielle pour l'architecture

By the end of the course, students will be able to understand and experiment with the fundamental principles of Machine Learning, and to critically assess its applications and implications in the field of architecture.

Theory

There is no prerequisite.

Introduction à l'intelligence artificielle pour l'architecture

Prerequisite or co-requisite units are specified within each programme.
 

Theory

The course consists of theoretical lessons presenting the fundamental principles of machine learning. 

Introduction à l'intelligence artificielle pour l'architecture

The course combines theoretical input and practical exercises aimed at introducing and experimenting with the fundamental principles of AI applied to architecture.

It is primarily based on individual work aimed at developing a critical perspective on AI, and generative AI (GenAI) in particular, in the field of architecture. The topic will be defined and approved in advance with the instructor. Individual and group supervision sessions will support the development of this work.

Theory

Face-to-face course


Further information:

The course is given during the first semester.

Introduction à l'intelligence artificielle pour l'architecture

Face-to-face course

Theory

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.

Introduction à l'intelligence artificielle pour l'architecture

Exam(s) in session

Any session

- In-person

oral exam

Written work / report


Further information:

Assessment is based on a report presenting the individual work developed during the semester. The report will be presented orally during the examination period.

Theory

All course information will be posted on eCampus.

Theory

Professor : Vân Anh Huynh-Thu.

Email : vahuynh@uliege.be

Office : 1.84b, B28 (Montefiore Institute, Sart-Tilman)

Introduction à l'intelligence artificielle pour l'architecture

aurelie.deboissieu@uliege.be