Visualization is a powerful way to understand and interpret machine learning--as well as a promising area for ML researchers to investigate. This tutorial will provide an introduction to the landscape of ML visualizations, organized by types of users and their goals. We'll discuss how each stage of the ML research and development pipeline lends itself to different visualization techniques: analyzing training data, understanding the internals of a model, and testing performance. In addition, we’ll explore how visualization can play an important role in ML education and outreach to non-technical stakeholders.

The tutorial will also include a brief introduction to key techniques from the fields of graphic design and human-computer interaction that are relevant in designing data displays. These ideas are helpful whether refining existing visualizations, or inventing entirely new visual techniques.