  Which topics of machine learning are most commonly addressed in research?
This question was initially answered in 2007 by doing a qualitative survey
among distinguished researchers. In our study, we revisit this question from a
quantitative perspective. Concretely, we collect 54K abstracts of papers
published between 2007 and 2016 in leading machine learning journals and
conferences. We then use machine learning in order to determine the top 10
topics in machine learning. We not only include models, but provide a holistic
view across optimization, data, features, etc. This quantitative approach
allows reducing the bias of surveys. It reveals new and up-to-date insights
into what the 10 most prolific topics in machine learning research are. This
allows researchers to identify popular topics as well as new and rising topics
for their research.
