Fix more typos in perc.tex

Author: bharat.raghunathan

book/perc.tex

@@ -79,7 +79,7 @@ \section{Bio-inspired Learning}
 example.  Otherwise it predicts a negative example.
 
 The weights of this neuron are fairly easy to interpret.  Suppose that
-a feature, for instance ``is this a System's class?'' gets a zero
+a feature, for instance ``is this a Systems class?'' gets a zero
 weight.  Then the activation is the same regardless of the value of
 this feature.  So features with zero weight are ignored.  Features
 with positive weights are indicative of positive examples because they
@@ -88,7 +88,7 @@ \section{Bio-inspired Learning}
 decrease.
 
 \thinkaboutit{What would happen if we encoded binary features like
-  ``is this a System's class'' as no=$0$ and yes=$-1$ (rather than the
+  ``is this a Systems class'' as no=$0$ and yes=$-1$ (rather than the
   standard no=$0$ and yes=$+1$)?}
 
 It is often convenient to have a non-zero \concept{threshold}.  In