· Data abstraction: types of data and of datasets
· Which visualisation for which task?
· Validating visualisations
· Display and ocular perception
· Visualisation channels (colour, size, shape, angle, ...)
· Tabular data: lists, matrices, tensors
· Spatial data: scalar, vector and tensor fields
· Networks and trees
· Link between machine learning and visualisation: clustering, dimensionality reduction, graph embedding
· Interactive visualisation
· Multiple views
· Advanced topics in visualisation
Due to the COVID-19 crisis, the information in this section is particularly likely to change.Lectures, practical sessions on computers, project.
All activities can switch from presential to comodal or distancial depending on sanitary conditions.
Due to the COVID-19 crisis, the information in this section is particularly likely to change.Oral Exam.Practical modalities depend on sanitary conditions.
- Slides of the course, available on Moodle