Data Information Management
This theme focuses on the central role of data and information management in knowledge-driven societies. With the exponential growth of data, accessing relevant structured and unstructured data from the web poses significant challenges. You will explore the scientific and practical aspects of data and information management, developing the skills to design, develop, and evaluate systems from both technological and human perspectives, enabling them to satisfy user needs in different contexts.
Year 1 |
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Quarter 1 |
Quarter 2 |
Quarter 3 |
Quarter 4 |
| Data management and Engineering | Software Engineering and Testing for AI Systems | Responsible Data Science and AI Engineering | Research course |
| Machine and Deep Learning | Theme 1 | Theme 1 | Theme 1 |
| Probabilistic AI and Reasoning | Theme 2 | Theme 2 | Theme 2 |
Credits: each course in a theme is 5EC, so each theme is 15EC.
Students choose 2 themes, each of which has 3 courses in the 2nd, 3rd and 4th quarters of the 1st year. For this theme, you will take the following courses:
Q2 - Web-Scale Data Management
This course addresses the challenges of Data Management at Web-scale. Especially, it covers the need for large-scale distributed data storage systems. The lectures therefore introduce step-by-step increasingly complex distributed storage systems, leading up to modern implementations of different NoSQL data storage systems. The advantages, disadvantages, and general properties of these systems are discussed in more detail. The course focuses also on database transactions, and the implications those have in modern, Web-scale application development and deployment.
Q3 - Information Retrieval
Information Retrieval (IR) is the discipline that deals with the representation, storage, organisation of, and access to information items, and it is concerned with providing efficient access to large amounts of unstructured contents, such as text, images, videos etc. The objective of this course is to introduce the scientific underpinnings of the fields of Information Retrieval. The course aims at providing you basic information retrieval concepts and more advanced techniques for efficient data processing, storage, and querying. Covered topics include: Information Retrieval Models; Indexing techniques; Web Search; Evaluation of information retrieval systems; Recommender Systems.
Q4 - Web Science and Engineering
The main subject of this course is the Web, and in particular Web Data. The course considers developments in the Web and the (big) data management challenges associated to it. In particular, the course considers the relationship between people and technology that come with the Web and Web-based information systems, both from an engineering perspective as well as from an analytical perspective. The course explains the concept of Web-based Information System and thus concentrates on a large class of modern information systems that use the web and web data in one way or another. It also considers research in social web data analytics and data science techniques to extract user knowledge from social web data.