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Interactive Machine Learning: Visualization & Explainability
Last Updated: 2026-06-03 00:14:09
Abstract
Visual Analytics supports the design of human-in-the-loop interfaces that enable human-machine collaboration. In this course, will go through the fundamentals of designing interactive visualizations, later applying them to explain and interact with machine leaning models.
Objective
The goal of the course is to introduce techniques for interactive information visualization and to apply these on understanding, diagnosing, and refining machine learning models.
Content
Interactive, mixed-initiative machine learning promises to combine the efficiency of automation with the effectiveness of humans for a collaborative decision-making and problem-solving process. This can be facilitated through co-adaptive visual interfaces. This course will first introduce the foundations of information visualization design based on data charecteristics, e.g., high-dimensional, geo-spatial, relational, temporal, and textual data. Second, we will discuss interaction techniques and explanation strategies to enable explainable machine learning with the tasks of understanding, diagnosing, and refining machine learning models. Tentative list of topics: 1. Visualization and Perception 2. Interaction and Explanation 3. Systems Overview
Resources
Lecture Notes
Course material will be provided in form of slides.
Literature
Will be provided during the course.
Learning Materials (Links)
- Main link
- Information
General Information
- Language
- English
- Levels
- MSC , WBZ
- Frequency
- Yearly recurring
Examination
- Type
- end-of-semester examination
- Mode
- written 120 minutes
- Aids
- None
Registration & Places
- Max Places
- 190
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise | Interactive Machine Learning: Visualization & Explainability |
|
3 h weekly |
| independent project | Interactive Machine Learning: Visualization & Explainability | No time listed | 1 h weekly |
Offered In
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Statistics Master (The following courses belong to the curriculum of the Master's Programme in Statistics. The corresponding credits do not count as external credits even for course units where an enrolment at ETH Zurich is not possible.)
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