Abstract

A lot of focus in the machine learning community is on the choice of algorithm. This talk will instead focus on the dataset. While most people have realized by now that dataset quality is critical for the success of machine learning projects, most teams still struggle to quantify data quality. This talk will provide hands-on examples of how to quantify dataset quality and reason about the impact we can expect various types of quality issues to have on model performance.

Daniel Langkilde

Co-Founder @ Annotell

Daniel Langkilde is CEO and co-founder of Annotell, which provides the analytics and annotation platform used to ensure the performance of autonomous vehicle perception systems. He has focused on the relationship between data quality and machine learning product performance for almost ten years. He has an M.Sc. in Engineering Mathematics and has been a Visiting Scholar at UC Berkeley and MIT. Before starting Annotell, he was Team Lead for Collection & Analysis at Recorded Future. Besides that, he is also on the Board of Directors at Chalmers University.