Courses

This course dives into creating interoperability across multiple servers and organizations, on multiple levels. You learn how to carefully reuse domain models where possible, and how to define your own terms where necessary, as Linked Data. Solid applies Linked Data on personal data management. Challenges that can be solved with Linked Data arise from the moment multiple apps read and write from the same storage. Techniques will be discussed to provide cross-app interoperability across open, shared, as well as personal knowledge graphs.

Past editions: 2024, 2023, 2022

Upcoming edition: to be announced, from 2025-09 till 2026-01.

Topics: Solid, Linked Data, Semantic Web, RDF, Ontologies, Knowledge Graphs, Web querying

Managing data on one machine for one specific kind of use is fairly straightforward. It is from the moment that that initial dataset needs to be shared with more than one application and needs to be combined with other datasets managed by other organizations on different machines, that more complex computer science and information technology problems arise. In this course we will deep-dive in the current state of the art in creating Knowledge on Web-Scale. Your personal data, data published publicly on the Web and data explicitly shared with you, becomes your Knowledge Graph that applications and services can use to assist you in your day to day activities. Let us take you on a quest to fully automate data integration.

Past editions: 2025

Upcoming edition: to be announced, from 2026-02 till 2026-06.

Topics: Linked Data, Solid, Semantic Web, Ontologies, Knowledge Graphs, Web querying

Artificial Intelligence (AI) has come a long way since its first use and application many decades ago. The use of AI and Machine Learning have seen an immense uptake in the 21st century. The techniques developed in the domain were and are successfully applied to a wide variety of problems, both in academia, private and public industry. As this domain became more and more established in recent years, new challenges arose, as AI are complex and sophisticated algorithms that sometimes make it difficult for the humans to understand and interpret the decisions or suggestions of the AI system. Explainable AI puts the following properties on the foreground to deliver trust:

1. gaining trust by explaining for example the characteristics of AI output;
2. explaining an AI technique to increase understanding, allowing to investigate if the technique can be transferred to another domain or problem;
3. informing a user about the workings of an AI model so that there is no misinterpretation;
4. establishing confidence of users by using AI models that are explainable, stable but also robust; and
5. raising privacy awareness when explaining AI models.

Past editions: 2024, 2023, 2022

Upcoming edition: to be announced, from 2025-09 till 2025-12.

Topics: Machine Learning, Hybrid AI, Ontologies, Knowledge Graphs

Digitale data vormen de motor van een zich ontwikkelende samenleving en overheid. Ook publieke organisaties beschikken over enorme hoeveelheden data. Sterk en doordacht datamanagement laat overheden toe om zowel de effectiviteit en efficiëntie van de interne processen als de kwaliteit van de externe dienstverlening voor burgers en bedrijven te verbeteren. Data zijn met andere woorden essentiële bouwstenen voor een overheid die op een open, evidence-informed, efficiënte en datagedreven manier wil functioneren, beleid voeren en maatschappelijke (meer)waarde wil creëren.

Steeds meer beleidsmedewerkers en ambtenaren verzamelen, beheren en analyseren data op een dagelijkse basis. Een zorgvuldig gebruik en beheer van data door overheden vergt een aanpak die verschillende invalshoeken combineert.

Met het gloednieuwe postgraduaat datagebruik en -management in de overheid bieden we een multidisciplinaire opleiding aan die ingaat op dit actuele vraagstuk, met een specifieke focus op de publieke sector. Het programma integreert en verbindt bestuurskundige, beleidsmatige, technologische, economische, juridische en ethische aspecten van datamanagement en -gebruik in een overheidscontext. Zo gaan we onder meer in op de volgende vragen:

• Wat houden begrippen zoals big data, blockchain, interoperabiliteit en cloud nu eigenlijk in en wat betekenen ze concreet voor overheden?
• Hoe zorg je ervoor dat data binnen jouw organisatie op een optimale en kwaliteitsvolle manier beheerd en gebruikt wordt?
• Welke data governance modellen, structuren en systemen vormen de fundamenten van doordacht databeheer?
• Welke (juridische) kennis en vaardigheden moet je als data-professional beheersen om beslagen op het ijs te komen?
• Hoe draag je zorg voor de veiligheid van gevoelige data en waarborg je de privacy van burgers?

Na dit postgraduaat ken je het antwoord op al deze vragen en kan je het gebruik en beheer van data binnen jouw organisatie naar een hoger niveau tillen.

Upcoming edition: from 2025-09-19 till 2026-03-30, more info.

Topics: Big data, Interoperability, Data governance, Privacy

In the manufacturing and process industry, we strive for almost full-time uptime of machines. However, it is inevitable that critical assets will fail sooner or later, such as pumps, compressors or robots. It is therefore of great added value to predict failure in time in order to be able to schedule a production stop and carry out appropriate maintenance or replacement. In recent years, the Digital Twin has evolved from a vague concept to a reality. A Digital Twin is defined as a virtual copy of a physical system, which evolves along with it thanks to an exchange of data and information. The combination of increased computing power, connectivity, data and AI has created realistic simulation models in recent years that have made the Digital Twin concept possible. In this course, we will delve deeper into the latest developments in the world of condition monitoring and Digital Twins and then bring both concepts together and demonstrate the added value of Digital Twins in condition monitoring.

Upcoming edition: from 2025-05-14 till 2025-06-25, more info.

Topics: Machine Learning, Hybrid AI, Ontologies