# Why do neural nets and machine learning tend to work well with MCTS, but not with regular Minimax game-playing AI?

I've often heard MCTS grouped together with neural nets and machine learning. From what I gather, MCTS uses a refined intuition (from maching learning) to evaluate positions. This allows it to better guess which moves are worth playing out more.

But I've almost never heard of using machine learning for Minimax+alpha-beta engines. Couldn't machine learning be used for the Engine to better guess which move is best, and then look at that move's subtree first? A major optimization of the minimax algorithm is move-ordering, and this seems like a good way to accomplish that.

$$$$UCT(v_i,v) = \frac{Q(v_i)}{N(v_i)} + cP(v_i, v)\sqrt{\frac{N(v)}{1+N(v_i)}}$$$$