Abstract: Over the past decade, astronomy has adopted a range of new techniques and methods for inferring knowledge from data. Among these, machine learning is quickly becoming a standard toolbox for working with large, complex data sets. While machine learning has certainly made many tasks easier, it also poses new challenges to astronomers: how do we interpret these models? How do we train them? How do we connect the outputs from ML models to the underlying astrophysics we ultimately want to know? How do we quantify biases and uncertainties? In this talk, I aim to set the stage and provide a starting point for discussions around how we use (and plan to use) machine learning in astronomy, and how we can make these models useful for astrophysical inference.
