Lets say I want to create a radio station that plays Hindustani classical music (wiki) but selects the right raaga based on local time and the season. The notes of the raagas are by definition correlated to natural moods. So basically it needs a library of notes labeled by time etc; the model then needs to listen to the music library and select tracks that match certain criteria.
Is this a machine learning problem?
For supervised learning, I understand that the criteria is that the answer needs to be known.
I have read @jeremy 's article about the drive train approach for data products and totally agree with looking for the ‘objective’; is it useful to create a more structured process flow for problem abstraction for the datascientist rather than the user’s point of view?