Kategorie: ‘Courses’
Bridge Course Databases
A blended learning bridge course for master’s students in Data Science, Computational Social Science, and related programs.
Datenbanken und Informationssysteme
Die Vorlesung “Datenbanken und Informationssysteme” gibt einen einführenden Überblick über Datenbanken und ihre Verwendung in Informationssystemen.
Bridge Course Databases
A blended learning bridge course for master’s students in Data Science, Computational Social Science, and related programs.
Research Focus Class: Industrial Applications for LLM-driven Agentic Systems
UPDATE: Unfortunately, we reached the limit of our employees capacity, therefore we are unable to provide more seats for this class. The application process is closed.
In this Research Focus Class (RFC), we would like to provide an environment for students to work on developing their own research ideas. With a guidance from research assistants, we will ask you to design and implement these ideas independently. Results of such classes may lead to identifying a Master’s thesis topic or publications in scientific venues. This class offers students the opportunity to gain hands-on experience through interactive and practical research in an innovative setting.
If you are interested in participating in this course, please send us an email (to rfc@dbis.rwth-aachen.de) with a brief description of your motivation for taking this course.
Image generated with AI.
Research Focus Class on Data Ecosystems
This research-oriented course is for students who are interested in current issues, developments, and research in the field of data ecosystems. A selected topic from one of the following areas of data ecosystems will be discussed: data sovereignty, data exchange, data protection, data security, FAIR data, etc.
First, there will be an introduction to the current state of research on the selected topic. Next, each student will be guided in identifying a question (research idea) within the topic area, familiarizing themselves with it, and presenting it to the other participants. This concept phase will be followed by a practical phase in which students will develop (prototype implementation, analysis, simulation, etc.) and evaluate their research idea. The exact procedure may vary from semester to semester and depending on the topic area.
Semantic Web
As part of the W3C Semantic Web initiative standards and technologies have been developed for machine-readable exchange of data, information and knowledge on the Web. These standards and technologies are increasingly being used in applications and have already led to a number of exciting projects (e.g. DBpedia, semantic wiki or commercial applications such as schema.org, OpenCalais, or Google’s Freebase). These technologies become even more important in the era of Large Language Models (LLMs) because they enable the integration and structuring of vast amounts of disparate data, making it more accessible and meaningful for AI systems to process and understand and grouding the LLMs output in facts, preventing hallucination.
In this course, you will gain hands-on experience with linked data technologies while exploring their theoretical background.
Bulding Large Language Model Applications Lab
Large Language Models (LLMs), such as GPT, Claude and Llama, are powerful tools that have transformed the landscape of Natural Language Processing (NLP), enabling advanced applications in various fields. The “Building Large Language Model Applications,” course is a practical, hands-on course designed to provide students with in-depth knowledge and experience in developing applications utilizing LLMs. Throughout the course, students will work on real-world projects and learn how to design, implement, and deploy advanced LLMs systems.
Data Visualisation and Analytics
This course provides participants with a comprehensive and versatile toolbox of data visualisation and analysis methods, which can be transferred to a vast number of applications.
Empathic Computing Lab
Empathic computing investigates how technology can support collaboration, shared understanding and human-centered interaction. In this lab, student teams design and develop their own projects using immersive and pervasive media—ranging from XR headsets and smartphones to tablets, laptops, and wearable devices. Projects may leverage open standards like WebXR, or use engines such as Unity, depending on the goals and platforms chosen. The focus is on creating meaningful, responsive experiences through thoughtful interaction design and emerging technologies.
Knowledge Graphs Lab
The Knowledge Graphs Lab offers a practical insight into structured semantic graphs, which model real-world entities and their complex relationships. By leveraging Knowledge Graphs (KGs), you can represent, integrate, and reason over heterogeneous information in a way that makes data more Findable, Accessible, Interoperable, and Reusable (FAIR).
Why Knowledge Graphs?
Knowledge Graphs power a wide range of applications—from enhancing search engine results (e.g., Google) and fuelling intelligent assistants (like Siri or Alexa) to driving recommendation systems and providing verifiable data backbones for Large Language Models (LLMs)—as they have proven to be scalable, flexible and extendable for storing heterogeneous knowledge across diverse domains.
What You Will Do
- Work in small groups to tackle real-world challenges.
- Design and implement software solutions – from semantically integrating diverse datasets into KGs to integrating KG data into application pipelines.
- Explore how KGs can improve downstream AI applications, such as enhancing the output of LLMs using approaches like GraphRAG
What You Will Gain
- Hands-on experience with popular frameworks for Knowledge Graphs and LLMs.
- Practical insights into building and leveraging KGs, including data modelling, query processing, and semantic integration.
- Teamwork and software development skills
By the end of this lab, you will have a deeper understanding of Knowledge Graph concepts, tools, and how the knowledge of KG can be integrated into real-world applications.