Research course

This course provides you with a solid understanding of empirical research methods. You learn to set up a research study, collect and analyse data, and draw scientifically valid conclusions. Emphasis is placed on reproducible research practices, including techniques like pre-registration, and creating a data plan. To make sense of data samples, you study how to make statistical inferences about the population. You explore frequentist and Bayesian data analysis approaches, gaining the ability to make meaningful statistical inferences based on collected data. The course covers working with different types of datasets, including high-dimensional data sets such as images and longitudinal data..

The course's main topics are:

• Conceptualizing research questions and experimental design, and data planning
• Frequentist and Bayesian data analysis
• Generalized linear models for statistical analysis
• Multilevel modelling for hierarchical and longitudinal data analysis
• Measuring and sampling, validity, and reliability
• Principles of statistical testing.

This course is about doing fundamental research in Machine Learning (ML) /Deep Learning (DL). You will do research to better understand ML/DL models, what assumptions they make, when they fail, how to pose research questions, answer empirical hypotheses experimentally and analyse results. In this course you study how to "understand" and do fundamental research in ML/DL itself. This course is about repeatedly asking "Why?"; it's about better understanding, reflecting, questioning, reproducing, and analysing ML/DL research. You will read, present, and debate scientific papers on machine learning scholarship. You will construct a logical research "storyline", create synthetic toy problems, reproduce existing research, while honing a critical attitude and being able to communicate concisely and clearly.

Bioinformatics is at the heart of many modern systems biology analyses and encompasses the application of statistics and computer science to (large-scale) biomolecular datasets. Bioinformatics is about smart ways of extracting knowledge from the enormous amounts of data that can be generated using modern measurement techniques. For instance, it plays an important role in finding the genetic origins of various diseases, such as cancer, diabetes, or Alzheimer.

In this course you study some key examples of relevant data-driven bioinformatics methods by reading and discussing a set of selected papers that present interesting DSAIT solutions with significant biological conclusions.

You can only select Research in Bioinformatics if you follow the Bioinformatics theme.

Intelligent decision making is a key skill of computational agents. Research on this topic focuses on building models and algorithms that enable AI systems to take appropriate decisions.

Building upon theoretical knowledge gained in the Probabilistic Decision Making or Reasoning and Search themes, students collaborate in small groups on a distinct research project per group, for instance on decision-making problems in transport, logistics or smart energy grids. Purely algorithmic challenges will also be provided. The research projects provide a good opportunity to learn about topics suitable for Master’s projects.

You can only select Research in Intelligence Decision Making if you follow the Optimisation & Reasoning and/or Probabilistic Decision Making theme.

Social Signal Processing and Affective Computing, aims to develop automated approaches that can interpret human social and/or affective behaviour through machine analysis and production of nonverbal and verbal behaviour. 

In this course you will learn how next-generation computing can make use of such social and affective signals by giving it the ability to recognize and produce human social signals and social behaviours. You will learn about relevant findings in social psychology, and computational techniques that allow systems to make use of social and affective signals to become more effective and more efficient by being able to detect but also simulate (e.g. in virtual agents) blinks, smiles, crossed arms, laughter. These techniques can be used in robots, virtual agents, smart homes, crowd monitoring, among others.

In this research course you conduct deep investigation of recent techniques solving attractive problems in Visual Computing and Interactive Visual Analysis including Visualization, Interaction, Image Processing, Inverse rendering, and 3D data processing. You will survey research outputs in the area of your interest, present a selected paper and develop an implementation supporting the education on the topic. The goal is to strengthen your analytical and presentation skills and prepare you for research in your MSc thesis project.

In this course, you will discuss recent developments in web data and information management, with topics on:

• web technology (e.g., web engineering, hypertext, adaptive web)
• web data management (e.g., web data interoperability, system, and data integration)
• web data and semantics (e.g., ontologies, semantic web, metadata)
• web data analytics (e.g., user modeling, web personalization, web information filtering and retrieval)
• social web (e.g., social web data analytics, social networking, human computing)
• web science (e.g., crowdsourcing, trust, data science)

You will discuss this content while learning about the role of scientific communication and about the scientific methodologies and approaches for conducting research in the area.

In this research course you will prepare and give scientific presentations on the basis of research papers about selected topics. You will also attend the presentations and participate in discussions on the papers presented. In addition, you will write a short survey about a topic in the area of web data and information management of your choice.

Through all the exciting recent advances in digital media technology and the rapid growth of social media platforms, multimedia content is increasingly embedded in our daily lives, gaining enormous potential in improving the traditional educational, professional, business, communication and entertainment processes. To be able to use this potential for transferring these processes into user-centric interactive multimedia applications, technology is required that can help us access, deliver, enrich and share rich-media content. This course provides insight into the state-of-the-art cross-disciplinary research efforts related to the development of such technology. The topics covered by the course include, but are not limited to, multimedia engagement (emotional and social signals and recommender systems), multimedia technology (language and speech technology, telepresence and VR, mobile), and multimedia experiences (Quality of Experience, collaboration), and Multimedia in the Generative AI Era.