I wouldn't say that it's "mostly unsupervised" since a crucial part of their training is done in a traditional supervised fashion on a database of games by professional players.
But it's certainly much more automated than having an hand-coded heuristic.
Humans also learn extensively by studying the games of experts. In Japan/China, even fans follow games from newspapers.
A game might take an hour on average. So a pro with 10 years of experience may have played/watched upwards of 10,000 games. However, it takes much less time to read a game that has already been played - so a 10 year pro may be familiar with say 100,000 games. Considering that each game has 200+ moves, that roughly is a training set of order 2 to 20 million positions.
AlphaGo's training set consisted of 160,000 games with 29 million positions, so the upper end estimate for humans is similar. More importantly, the human training set is far more carefully curated and thus of higher quality.
DeepMind's go AI, called AlphaGo, has beaten the European champion with a score of 5-0. A match against top ranked human, Lee Se-dol, is scheduled for March.