# The Top 10 Topics in Machine Learning Revisited: A Quantitative Meta-Study

## TL;DR

This study quantitatively analyzes 54,000 machine learning abstracts from 2007 to 2016 to identify the top 10 research topics, providing an unbiased, comprehensive overview of prevalent and emerging areas in the field.

## Contribution

It introduces a large-scale, data-driven approach to identify the most prominent machine learning topics, moving beyond qualitative surveys to a more objective analysis.

## Key findings

- Identifies the top 10 machine learning topics from 2007-2016.
- Provides insights into emerging and declining research areas.
- Offers a holistic view across models, optimization, data, and features.

## Abstract

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.