I participated in the Kaggle DecMeg competition and it was a great experience. I came 114 out of 273 :) I approached it from a image processing point of view than the machine learning perspective. From comparing the winning codes, it can be seen that deep-learning approach wins by a huge margin. My question here is that do we really learn anything from applying the machine learning method. I understand that machine learns the characteristics of the data perfectly well. However, does that mean that we can get a good understanding of the underlying physical phenomeanon?
Here is a pictorial representation of the competition.
Comparing my code to the winners I find that my way of thinking is not really what the machine learning people think. It was a great machine learning experience! My code is in GitHub now.