My was composed by a dissertation and a project. I went extra mile, it wasn't required. In my dissertation I "leisurely" verified that the general problems of

My was composed by a dissertation and a project. I went extra mile, it wasn't required. In my dissertation I "leisurely" verified that the general problems of collaborative filtering did appear also in a specific music dataset (that for the sake of secrecy I'm not going to reveal,but you can consider a music dataset as big as the one available in iTunes Store 5 years ago). My point was, if you like A and B, and another user like A,B,C, is not necessary true that you have to like or be exposed to C as well. Later on I decided to try out and implement a better solution. In short it was on paper a behavior driven solution for matching preferences, with an underlaying layer of audio analysis and a collaborative fallback. If I look back at it, honestly talking, the implementation was a mess and probably biased towards the dataset. I did a Master in ML + AI applied to Multimedia with a focus on audio signals. I went trough the same idea, this time with a more scientific approach. Graduated. Patented part of the algorithm, sold to a company, hired by the same company, integrated in their system, and moved on :) In other words, pick something that you are passionate about, it maybe turn into a shortcut into the work world and give you a starting position much higher than any other grads.