AI education at Uni.lu
Artificial Intelligence is an important topic of study at the University of Luxembourg, featured in various programmes across different faculties. Students can explore AI concepts and applications through a range of courses and research projects, gaining essential skills and knowledge in this dynamic field. Our Bachelor, Master and Doctorate programmes are being constantly updated to reflect AI trends.
Our interdisciplinary approach ensures that AI education is integrated into multiple disciplines, including computer science, engineering, mathematics, law or social sciences. This broad exposure helps students understand the diverse applications and implications of AI.
Our bachelors including courses on AI
Several bachelor programmes at the University of Luxembourg integrate AI into their curriculum, equipping students with essential skills in machine learning, data analysis and intelligent systems. Below, you’ll find a list of these programmes, along with the AI-related courses they offer and the learning objectives that prepare students to apply AI across diverse fields.
- Big Data: The course is about (classical and new) techniques that are involved in the Big Data paradigm. The main goal is to spark the discussion about the Trade Offs between the classical data processing techniques and the upcoming ones for big data.
In addition, students should get basic knowledge on how to automatically process and analyze huge amount of data. - Introduction to Machine Learning: This course introduces Machine Learning (ML) principles and its three main learning paradigms (supervised, unsupervised, and reinforcement learning). For each learning paradigm, it presents some of its most typical foundational models and discuss them from the perspective of representation, evaluation, and optimization.
A special attention is given to a basic introduction into deep learning techniques and generalization. The course mixes theoretical concepts with vanilla implementations of various ML models.
- Intelligent Systems 1: This course aims to offer a foundation, ideas, and techniques underlying the design of intelligent agents and their application in various real-world domains.
Also, it offers different ways of system implementations with “intelligent” functionality through statistical and decision-theoretic modelling paradigms where agents will draw inferences in uncertain environments and optimize actions for arbitrary reward structures. Students will learn to recognize when intelligent functionality and artificial intelligence may be a good solution to a problem and to select appropriate AI methodologies and strategies.
Further, they will acquire knowledge enabling them to develop the necessary skills to design and implement an intelligent system. - AI for Education: In recent years, the term artificial intelligence (AI) has taken on a new meaning. While the original idea of AI is still to understand and artificially simulate human (cognitive) intelligence, the applications of AI have become increasingly important in recent years.
There is currently a real spirit of optimism, more and more new AI companies are being founded, governments, industry and science are investing in research and development projects and in targeted knowledge transfer, and with ‘AI for the common good’ and ‘AI for humans’ we are all prepared for future developments. One of these developments relates to education (and training), be it in schools and universities, in industrial training or in the service sector and customer service. The associated opportunities, but also risks, raise the questions of techniques (human-computer interfaces, intelligent systems) of the extent to which AI can be used sensibly and responsibly for learning and knowledge acquisition.
AI-based systems should not necessarily be seen as a replacement for existing learning practices and learning techniques, but as a supplement. The spectrum of ideas affected by this is therefore diverse. The aims of this course are to gain an overview of applications in the relevant area and to explore your own ideas.
- Artificial Intelligence for Smart Technologies: Introduction to artificial intelligence (data preparation, data analysis, estimation, classification) – AI projects. Analyse presentations as part of a series of lectures on artificial intelligence and its applications in engineering.
- Artificial Intelligence for Smart Technologies: Introduction to artificial intelligence (data preparation, data analysis, estimation, classification) – AI projects. Analyse presentations as part of a series of lectures on artificial intelligence and its applications in engineering.
Our masters including courses on AI
Our master programmes integrate advanced AI topics to prepare students for research, innovation and industry applications. These programmes offer specialised courses in areas such as machine learning, natural language processing and data-driven decision-making. Below, you’ll find an overview of the master programmes that include AI-related courses, along with course titles and learning objectives that reflect the depth and scope of AI education at the University of Luxembourg.
- AI and Cybersecurity: The objective of the course is to make the students familiar with the quality and security threats to AI systems, especially in light of (European) regulations. The course generally introduces the students to the foundations of security attacks, but enables also the manipulation of the related concepts through experiments (via Jupyter notebooks). The covered topics include: evasion attacks on computer vision, tabular data, NLP models; poisoning attacks; privacy concerns and threats; distribution drifts, presentation attacks on biometric systems, vulnerabilities in AI-based malware detectors, certifiable robustness, detection of generated content, regulation and auditing, etc. The course sessions will feature ex-cathedra presentations from the teaching team and external speakers, focused discussions, hands-on exercises, expert panels, paper reading and presentation by the students.cations in engineering.
- Intelligent Systems – Agents and Reasoning: The objective of this course is to provide a solid foundation in intelligent systems, with a focus on symbolic techniques for structured knowledge representation and agent reasoning.
- Intelligent Systems – Machine learning: The objective of this course is to provide a solid foundation in intelligent systems, with a focus on symbolic techniques for structured knowledge representation and agent reasoning.
- Introduction to Deep Learning: This course provides students with a high-quality and informed understanding of Deep Learning (DL) models for developing AI-based applications. The course promotes problem solving via design thinking philosophy. The course helps students to develop state-of-the-art competencies to solve many different real-world problems using DL.
The course will provide students with a competitive advantage to solve challenging research problems that require dealing with complex search spaces, non-linear relationships within the data, and flexible models that can scale up to thousands or millions of observations. These competencies are also of key importance to many industrial and technological companies.
Finally, the course also promotes development of soft skills such as written and verbal communication skills, via final project presentations. - Selected topics in Artificial Intelligence: The objective of this course is to introduce students to specific research topics in Artificial Intelligence/Logic, and to prepare them for individual research work (e.g. a Master/PhD thesis) by letting them study and discuss relevant research literature, possibly supplemented by small research tasks.
- Philosophy & Ethics of AI
- Computer Vision and Image Analysis
- Machine Learning: This course covers the fundamental principles of artificial intelligence (AI) technologies that are the core drive of various applications such as machine translation, autonomous driving, speech recognition, face recognition, and automatic scheduling.
The objective of this course is to equip the students with necessary tools and skills that help them tackle emerging and existing AI problems. Moreover, the students will also learn about the limitations and ethical concerns of AI.
- Intelligent Systems – Agents and Reasoning: The objective of this course is to provide a solid foundation in intelligent systems, with a focus on symbolic techniques for structured knowledge representation and agent reasoning.
- Introduction to Deep Learning: This course provides students with a high-quality and informed understanding of Deep Learning (DL) models for developing AI-based applications. The course promotes problem solving via design thinking philosophy. The course helps students to develop state-of-the-art competencies to solve many different real-world problems using DL.
The course will provide students with a competitive advantage to solve challenging research problems that require dealing with complex search spaces, non-linear relationships within the data, and flexible models that can scale up to thousands or millions of observations. These competencies are also of key importance to many industrial and technological companies.
Finally, the course also promotes development of soft skills such as written and verbal communication skills, via final project presentations. - Selected topics in Artificial Intelligence: The objective of this course is to introduce students to specific research topics in Artificial Intelligence/Logic, and to prepare them for individual research work (e.g. a Master/PhD thesis) by letting them study and discuss relevant research literature, possibly supplemented by small research tasks.
- Computer Vision and Image Analysis
- Taking a course in “Optimisation and Numerical Probability” is crucial for understanding how modern AI systems are optimised. Gradient descent methods, for instance, are essential for training neural networks as they help to minimise loss functions and efficiently adjust model parameters.
- Similarly, a course in “Programming with R and Python” is essential for anyone pursuing a career in AI and data science. Python is the dominant language for building machine learning models thanks to its extensive range of libraries, such as TensorFlow, PyTorch and scikit-learn.
- A course in “Fundamentals of Statistical Learning” is essential for grasping the theoretical principles of machine learning. It covers core concepts such as regression, classification, overfitting and model selection, which are essential for building models that generalise well to new data. By grounding AI methods in statistical principles, students learn not only how to apply algorithms but also understand when and why they work. This knowledge is critical for developing robust and interpretable AI systems.
- Studying “High Dimensional Statistics” is also essential because modern AI systems often process data with a vast number of variables, such as images, texts, or genomic sequences, and traditional statistical methods often fail in these settings, leading to overfitting and unreliable predictions. Traditional statistical methods are ineffective in such settings, resulting in overfitting and unreliable predictions. High-dimensional statistics provides the theoretical and practical tools needed to analyse, regularise and extract meaningful patterns from complex data. This makes it a cornerstone for building robust and efficient AI models.
- A solid foundation in “Mathematical Statistics” is also crucial, as it provides the theoretical basis for many machine learning algorithms. Concepts such as estimation, hypothesis testing and asymptotic behaviour help us to understand model uncertainty, evaluate performance and ensure the reliability of AI systems. Without this rigorous framework, it is difficult to develop or critically assess the statistical models used in AI.
- The Master’s in Data Science also offers a variety of practical “Machine Learning” courses (Introduction to Machine Learning Methods and Data Mining, Advanced Topics in Applied Machine Learning, Programming Machine Learning Algorithms for HPC, Introduction to Deep Learning, Big Data Analytics, Natural Language Processing in Data Science, etc.). These courses are vital for studying AI, as they allow students to implement theoretical concepts in real-world scenarios. Working with real-world datasets, deploying models and evaluating their performance provides students with a deep, intuitive understanding of how AI techniques function beyond abstract theory. This hands-on experience is essential for honing the competencies required to design, optimise, and evaluate machine learning systems for practical AI applications.
- Artificial Intelligence
- AI and Cybersecurity: The objective of the course is to make the students familiar with the quality and security threats to AI systems, especially in light of (European) regulations.
The course generally introduces the students to the foundations of security attacks, but enables also the manipulation of the related concepts through experiments (via Jupyter notebooks). The covered topics include: evasion attacks on computer vision, tabular data, NLP models; poisoning attacks; privacy concerns and threats; distribution drifts, presentation attacks on biometric systems, vulnerabilities in AI-based malware detectors, certifiable robustness, detection of generated content, regulation and auditing, etc.
The course sessions will feature ex-cathedra presentations from the teaching team and external speakers, focused discussions, hands-on exercises, expert panels, paper reading and presentation by the students.
- Introduction to Artificial Intelligence for the citizen: The objectives of this training are:
– to understand the Artificial Intelligence landscape and the position of deep learning within it
– to play with some accessible artificial intelligence experimenting the various techniques in different application domains: Text generation (e.g. ChatGPT), Object recognition in images or videos, Handwriting recognition, Generation of images from text, Automatic translation of text in all languages, Speech recognition, Speech synthesis.
– to understand and practice the social and ethical dimensions of artificial intelligence
– to understand and practice the impact of AI on society and on the job market
to understand through practice all the important notions which constitute the basis of the field of deep learning (neural network, weight, activation function, batch, bias, cost function, dropout, epoch, forward/backward propagation, gradient descent, hidden layer, parameters, hyper-parameters, input, output, learning rate, dataset, data augmentation, training, validation, test datasets, architecture, ANN, CNN, RNN, GAN, Transformers)
- Introduction to Artificial Intelligence for the European Citizen:
– Formulate and implement AI strategies in organizations, ensuring alignment with overall business goals and compliance with ethical and legal standards.
– Utilize AI for advanced data analysis, drawing meaningful insights from large datasets to inform decision-making processes in various sectors.
– Develop and propose policies and regulations for AI usage that balance innovation with ethical considerations and public welfare.
– Ensure that AI applications and projects adhere to ethical guidelines and principles, minimizing risks and maximizing societal benefits.
– Evaluate the potential impact, feasibility, and effectiveness of new AI technologies and applications in different sectors.
– Identify and manage risks associated with AI deployments, including privacy concerns, data security, and potential biases in AI models.
– Facilitate collaboration between government, private sector, and academia in AI initiatives, fostering innovation and knowledge exchange.
– Work on international projects and collaborations to develop and standardize AI technologies and practices globally.
- Artificial Intelligence for Languages & Cultures: The objectives of this introductory course on Artificial Intelligence (AI) for non-scientists and non-computer scientists are multifaceted.
Firstly, it aims to explain the foundational concepts and the current landscape of AI, with a particular emphasis on natural language processing using large language models.
The course also seeks to analyze and address the unique challenges posed by multilingual natural language processing through AI, including the application of multilingual machine translation techniques. Furthermore, students will learn to evaluate the effectiveness of cross-lingual transfer learning methods and utilize various tools and platforms designed for multilingual AI solutions.
Finally, the course will emphasize the identification and mitigation of cultural biases in AI, the enhancement of cross-cultural communication through AI, and the navigation of ethical and legal implications associated with AI in diverse cultural contexts. - Exploring AI
- Philosophy & Ethics of AI
AI4All: MOOCs hosted at the University of Luxembourg Competence Centre (ULCC)
The ULCC offers modular, flexible AI training through MOOCs and in-person workshops that are aimed at professionals across sectors like finance, space, cybersecurity and green economy.
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MOOC Machine learning in Weather & Climate
Uncover the future of numerical weather and climate predictions
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MOOC Elements of AI
Demystify Artificial Intelligence, learn more about AI, what it is and what it can and can’t do
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MOOC Machine Learning in an Industrial Environment
Create predictive models for future events with real-world datasets
The University of Luxembourg’s Institute for Digital Ethics (ULIDE) supports integrating ethical principles into AI education to prepare students for societal challenges like fake news, algorithmic bias and inclusivity:
University of
Luxembourg Institute for Digital Ethics
