Is Machine Learning a useful tool for analyzing experimental data? Apart from being a student of Physics I'm also a developer and recently in the context of cloud development I've been hearing a lot about cloud based Machine Learning. After looking at some examples of it I found it quite impressive.
With Machine Learning we can process lots of data and we can have the computer analyze it in many ways. As I saw we can use this to detect patterns, find anomalies in the data and so forth.
Although I'm much more interested in theoretical Physics and I don't understand much about experimental Physics I believe that as experiments in Physics gets more complex the analysis of the gathered data also increases in complexity.
This made me wondering if those methods of cloud based machine learning can be used in Physics to analyse experimental data. Is this something that's been used recently or it has no use at all? If it has no use at all, why is that the case?
 A: Machine learning is particularly useful in situations where the underlying model connecting input and output is either very complicated, or not understood, or both. Another situation in which machine learning can be useful is if the quantity of data is greater than the researcher (or team) can look at directly. Neither of these circumstances is very palatable to the typical physicist. Nearly all of what we would call physics consists of fairly simple models, and the urge to construct and understand simple models drives much of what we do. And, as far as the second point is concerned, collecting data has costs associated with it, and so most people don't want to collect more data than they will be able to directly use. 
There are two major exceptions to this: observational astronomy (and in particular, big survey projects like SDSS) and high energy experiments like LHC.  In both of these cases, machine learning has been used to good effect. (For example, see the Kaggle competitions for detecting the Higgs and classifying galaxies.)
Another promising application is in turbulence modeling (currently under development by Karthik Duraisamy and his group at the University of Michigan). Subgridscale turbulence models are already statistical, so it makes sense to use statistical learning methods to refine them.
Personally, I agree with you that machine learning ought to have a place in the physicists toolbox. We are, in general, comfortable with statistical descriptions of nature; we make heavy use of approximation methods; we would like to think we know how to make careful measurements, handle data, and propagate and account for uncertainties; and we have long been aware that there are situations in which a computational approach is the best (if not only) approach to a problem.
