The short answer: I also did a masters in a Machine Learning department, concurrently.
The long answer: I did a non-technical undergraduate degree. My research in philosophy of science made me intensely curious about causation, and how we could learn about causal relationships from observational data, or from confounded experimental data. Unfortunately, without a background in statistics, I couldn't pursue those interests very far.
I went looking for a grad program that would let me learn statistics. I was amazed to learn that Carnegie Mellon's Department of Philosophy focused on causal questions. In fact, Professors Clark Glymour, Peter Spirtes and Richard Scheines helped found the field of causal search – which is now a niche area of machine learning. If the mark of a successful philosophical research program is that it spreads outside of philosophy, causal search has been wildly successful.
The CMU Philosophy Department allowed me to take as many classes as I wished in the statistics and machine learning departments (including 10-701, 36-705 and 36-702). Thanks to their support, I have developed the skills I needed to pursue the questions that fascinate me.
Disciplinary labels don't help us do interesting work.