The main benefit is that you can prevent cryptogenic strokes, which are often caused by undiagnosed atrial fibrillation: http://www.nejm.org/doi/full/10.1056/NEJMoa1311376
For machine learning, I think you're on exactly the right path. The Udacity TensorFlow course taught by the Google Brain team is good; if you can complete that, you'll be as good as anyone at applying neural networks. The Stanford courses (http://cs231n.stanford.edu/, http://cs224d.stanford.edu/) are likewise great, as is Chris Olah's blog for intuition (http://colah.github.io/).
And down the road, if you're ever looking for an internship, definitely let us know! [email protected].
From what you're gathering, do you imagine an eventuality in which a medical device can take a form factor similar to these popular consumer choices and provide a signal of diagnostic quality? Or do you think PPG-style sensing is limited to a fundamental degree of uncertainty with respect to arrhythmias?
My guess is that by the time we get to the Apple Watch or Android Wear 3 or 4, it'll include a built-in ECG sensor. There are some devices out there already (like Nymi) that have built ECG into a wristband -- if the user touches a wristband worn on one hand with a finger on the other hand, that completes a circuit through the heart and gives you a 1-lead ECG.
I think the downstream implication is that, just like smart phones replaced dedicated GPSes and music players, many medical use cases will become "apps" that run on general purpose wristbands. The big advantage is that since they'll be always-on and non-intrusive, we'll be able to catch conditions much earlier.
And since you offered to answer questions, I have a somewhat niche one. I'm an MD/PhD student doing my research in computational electrophysiological modeling, and I'm getting interested in the idea of using machine learning in medicine, specifically in signals analysis (i.e. EKG), and in time-series analysis (like heart rate variability). I'm teaching myself machine learning via a fantastic UW Coursera series that covers the basics (regression, classification, clustering, etc) with good mathematical rigor, but I'm not really sure where to go from there. What techniques would you suggest I look at? Would learning something like TensorFlow (I know Google has put out a course on Udacity) be a good idea? Obviously at some point, I just need to find some small projects to jump in on, but I want to build a decent skillset first if I can. Any thoughts would be appreciated!