I had a brief experience with machine learning by using a clustering algorithm, i also read the basic ideas and calculations of a simple classification algorithm. Now, i would read more about "machine learning" and I found many similar definitions like the following:
"Machine learning is the science of getting computers to act without being explicitly programmed..."
"Machine learning is a type of artificial intelligence (AI) that allows software applications to become more accurate in predicting outcomes without being explicitly programmed... "
My understanding is that the ability to learn and adapting deduction (output) without reprogrammation is the main idea, and based on my personal understanding, this "adaptation" is only possible with "supervised algorithm" with a new training which can permit a change/adaptation/improvement on the output model with the same program and source code.
So based on my understanding again, this "adaptation" and "learning" definition doesn't fit unsupervised machine learning algorithms, since the model with all calculations is fixed and implemented! Any change will need an update on source code!
Therefore, I would have corrections to my misinterpretation, and more clarifications to have a better understanding of "machine learning" and unsupervised/supervised learning relation.