http://i.imgur.com/ny3RhD4.png
My guess is that Sedol won because he introduced sufficient complexity through cutting points and numerous black groups (see the image). Since AlphaGo uses Value and Policy networks to determine the hot spots to analyse using Monte Carlo tree searches, by making a game rife with lots of simultaneous fights, Sedol dodged the one-two punch of Value and Policy networks combined with MCTS.
In other words, if Sedol can make over a dozen points of interest on the board, AlphaGo cannot deeply assess them all. In the image, there are at least 13 interesting moves and cuts plus up to 15 groups (depending if lone stones are considered groups by AlphaGo). I suspect that this position was far more complex than at any point during any of the three previous games.
It might also explain the meltdown of playing out an unfavourable ladder (the P10 group, as P8 is another possible move).
https://en.wikipedia.org/wiki/Go_and_mathematics#Game_tree_c...
Eventually, math wins. There will come a point where humans cannot make the game sufficiently complex to beat a domain-specific machine intelligence (such as AlphaGo).