5 credits
30.0 h + 30.0 h
Q1
Teacher(s)
Lee John;
Language
English
Content
· What and why information visualisation?
· 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
· Reducing items and attributes: feature selection and dimensionality reduction
· Interactive visualisation
· Multiple views
· Advanced topics in visualisation
· 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
· Reducing items and attributes: feature selection and dimensionality reduction
· Interactive visualisation
· Multiple views
· Advanced topics in visualisation
Teaching methods
Lectures, practical sessions on computers, project
Evaluation methods
Oral Exam
Online resources
Moodle page of the course
Bibliography
- Slides of the course, available on Moodle
Teaching materials
- Slides of the course, available on Moodle
Faculty or entity
EPL
