We could certainly rattle off more terms :) Kernel density estimation? Deep learning? Semi-supervised learning?
But it's amazing how far you can get with some simple scatterplots, histograms and linear/logistic regressions.
a) Most of the time, more data beats better algorithms: http://anand.typepad.com/datawocky/2008/03/more-data-usual.h...
b) Even those seemingly simple things get complicated when you have a lot of data: http://komarix.org/ac/lr
So, someone taking Statistics 101 is considered to know machine learning in the startup circus? (Not that this would really surprise me, given the tendency to appropriate terms that had a certain meaning for purposes that have only a passing similarity to the original meaning.)