Interactive Visual Analysis
This theme discusses principles from visualization, visual analytics, human computer interaction, perception, and effective data communication using machine learning and AI methods and discusses how these principles are applied to interactive data exploration. The theme explores how visual data representations are tailored to different tasks and audiences, like medical practitioners.
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 - Data visualization
Data visualization is the visual representation of data by computer generated images. In this course we focus on human principle for visual analysis of various data collections such as databases. The goal is to improve insight, understanding and/or communication of data. Data visualizations use a combination of methods from a very diverse variety of disciplines: perception, computer graphics, human computer interaction, machine learning, numerical analysis, optimization, etc. Topics covered: models of the visualization process; colour models and use of colour; information visualization; representation and processing of data; interactive visual data analysis; truthful data visualization.
Q3 - 3D Visualization
This course builds upon the Data visualization course and extends the focus on scientific data, for example theory and practice of spatial data visualization such as medical scans or simulations. This includes the following aspects: data acquisition basics, clinical practice; image processing, e.g., filtering, segmentation, and measurement; medical volume visualization; illustrative visualization; advanced visualization for complex modalities; interaction techniques for 3D; advanced applications and specific requirements for different fields.
Q4 - High-Performance Analysis Systems
High-Performance Analysis Systems ties the previous two courses together but extends the horizon towards a practical realization in the context of large-scale analysis. This course covers systems for user interaction with data that puts a special emphasis on performant implementation. The data sets can be results of numerical simulations or measurements (scientific visualization), or other data collections such as databases (information visualization). The goal is to improve insight, understanding and/or communication of data. Therefore, three parts are taken into account: (1) Computer-Human Interaction: How to design effective and efficient interfaces. (2) High-Performance Computing: How to programme algorithms that can process and visualize (large) data sets fast. (3) Analytics and Applications: How and where to implement such systems.