I gave at talk to Berkeley undergraduates at the Machine Learning at Berkeley (ML@B) general meeting. I talked about work in progress and we had a great discussion interpreting the results. They asked great questions about both technical details of the work and the broader implications and positioning of the work.
The abstract I provided for the talk is here: In the age of Big Data, we now have data for an abundance of new concepts that have been historically studied only qualitatively. Data science tools make working with data accessible to those even without a background in the underlying statistics. Together, these facts mean that the way that machine learning algorithms are being used is often quite different from how the use cases imagined when they were designed. My work aims to answer the question, how to we need to adapt or augment machine learning algorithms to facilitate data driven discovery in these domains? In this talk, I’ll frame some of the common technical challenges in my work and show preliminary results on tools I’m building to augment ML algorithms.