Interactive Machine Learning: Visualization & Explainability

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