Machine learning (in particular deep learning) started to become extremely hot 5–10 years ago. Now, this trend has fully reached most academic fields outside computer science as well. But will it also help you with your research question?
It is important to notice that this point of “outperforming current measures”* is broader than only achieving higher accuracy. Often machine learning can also provide faster, more scalable solutions. In big data times, that can make a huge difference.
Part 2 — Will it make sense for your particular problem/data?
Now we reach the difficult part. If you have never applied any machine learning yourself, this seems near-impossible to answer. The second best thing you can do (first best thing again is: talk to someone experienced with machine learning!**), is to search for related work where machine learning was applied. This is tedious, and you might have to dig your way through mountains of jargon and unnecessarily complex and incomprehensible papers. Going through this pain, however, will hopefully give you a more realistic picture of what you can obtain with the type and amount of data you have. Otherwise, those incredibly shiny results that make the headlines can easily cause unrealistic expectations. As an orientation, have a look at the flowchart below:
Quick check if you are onto something with you machine learning idea. Don’t forget, it’s a flowchart, not an expert. If you come across any question where your answer is neither Yes or No, but “I don’t know”, you unfortunately have to suffer a bit more and read another 5 related blog posts, tutorials, or papers (the latter if you like suffering). Or, maybe I repeat myself…, just talk to someone with more experience in machine learning. **Hint: **People that have experience with machine learning tend to drink coffee. If they don’t, they drink tea. Asking someone with machine learning experience to discuss an idea over a cup of coffee/tea will probably help to finish this flowchart. [by Florian Huber, CC BY 4.0, see https://zenodo.org/record/4010272 for high-res pdf]If you are serious about using machine learning for your research — maybe you are applying for funding, or deciding on future steps in you project, or looking for new collaborations? — better spend some time to gain a basic intuition on how machine learning could help with your problem. Don’t worry, you don’t have to become a machine learning expert yourself, but I imagine you also don’t want your next proposals to sound like this:
“We have data (or hope to have it), and then we want to do magic (which we simply call deep learning).”
A little magic at the end
OK then. No magical solutions from deep learning. Still, I am convinced that there is plenty of opportunity to enhance scientific research through machine learning. **Yes, it can be hard to do right.
And no, it is no silver bullet.
But it is a whole lot of fun to do! And even if it doesn’t achieve what was hoped for, it will give you new insights about your data and your research question.
And sometimes… well… sometimes… there are those rare moments when you press enter and then start to see this magical glittering that seems to come from your screen.
Get in touch
I hope you find this quick guide helpful. If you do, please share it, clap for it, or even better: use it for your upcoming machine learning journey! And get in touch if you have any comments or questions.
If you are based at a Dutch research institution and have a research question where you have hope that machine learning could help improve things, speed up things, or give access to bigger datasets, but feel that you need additional feedback on your ideas: feel free to get in touch with the machine learning team at the [Netherlands eScience Center]: machine-learning@esciencecenter.nl
You also find me on twitter: me_datapoint**
Special thanks to [Patrick Bos,] [Sonja Georgievska,] [Tom Bakker,] [Carlos Martinez-Ortiz] and [Pablo Rodríguez-Sánchez] for helpful comments and discussions.
