To the best of my understanding, Monte Carlo Search is an alternative method to Minimax for searching a tree of nodes. It works by choosing a move (generally the one with the highest chance of being the best), and then performing a random playout on the move to see what the result is. This process keeps continuing for however much time is allotted.
This doesn't sound like Machine Learning, but rather a way to traverse a tree. However, I've heard that AlphaZero uses Monte Carlo search, so I'm confused. Is using Monte Carlo search why AlphaZero learns? Or did AlphaZero do some kind of machine learning before it played any matches, and then use the intuition it gained from Machine learning to know which moves to spend more time playing out with Monte Carlo search?