Visualisation for and with AI

Day 1: Data Visualisation Principles and Techniques

Instructor: Prof. Katrien Verbert (KU Leuven)

This day introduces the core principles and techniques of data visualisation. Participants will explore how human perception affects data interpretation, learn effective methods for data representation, and understand the role of interaction in enhancing visualisations. The day will conclude with a practical Tableau tutorial, allowing participants to apply these concepts hands-on.

Day 2: Visualisation for Data Science & AI: Data Preparation & Model Quality Evaluation

Instructor: Prof. Jefrey Lijffijt (UGent)

This day focuses on the integration of visualisation in the machine learning development pipeline. Participants will learn about the role of visualisation in data preparation and model quality evaluation. Key topics include:

• Exploratory Data Analysis (EDA): Using various plots such as histograms, density/KDE plots, raincloud plots, empirical CDF plots, correlation heatmaps, and pair plots.
• Dimensionality Reduction (DR) Methods & Representation Learning: Understanding captured information, the elbow method, and silhouette analysis.
• Handling Missing Values: Creating missing value plots.
• Outlier Detection: Techniques like local-outlier factor and tools like Cleanlab.
• Contrasting Data & Distributions: Effective methods for comparison.
• Model Evaluation: Using AUC-ROC, PR curves, hyper-parameter sensitivity, calibration, and monitoring tools like Weights & Biases and TensorBoard.
• Bias and Fairness: Examples and toolboxes, including the visual auditor.

Day 3: Human-Centered Explainable AI

Instructor: Prof. Katrien Verbert (KU Leuven)

This day is dedicated to the human-centred aspects of explainable AI. Participants will delve into:

• Human-Centered Explainable AI: Understanding the importance of making AI systems interpretable and user-friendly.
• Interaction Design: Techniques for designing effective user interactions with AI systems.
• Cognition: Gaining insights into how users perceive and understand AI outputs.
• Human-Centered Design Methods: Approaches to designing AI systems with the user in mind.
• Evaluation Methods: Techniques for assessing the effectiveness and usability of AI systems.
• Hands-On Activity: Practical exercises to apply the concepts learned throughout the day.

Day 4: AI for Visualisation: Dimensionality Reduction and Automation

Instructor: Prof. Jefrey Lijffijt (UGent)

The final day explores the application of AI in data visualisation, focusing on dimensionality reduction and automation. Key topics include:

• The why/what/how of visualization & what parts of the data visualization pipeline can we automate? Discussion of survey papers & practical examples: what to look at and how to look at it (approaches using traditional ML/optimization as well as GenAI).
• Dimensionality Reduction (DR): Linear and non-linear DR methods (PCA, LDA, autoencoders, MDS, and t-SNE), why there exist so many methods, categorizing them and learning when to use what type of method.
• Representation learning as an intermediate step, with practical examples of graph data.