diff --git a/book/thy.tex b/book/thy.tex
index 265d639..31f6607 100644
--- a/book/thy.tex
+++ b/book/thy.tex
@@ -161,7 +161,7 @@ \section{Probably Approximately Correct Learning}
 at most $5\%$ error.  If this situtation is guaranteed to happen, then
 this hypothetical learning algorithm is a PAC learning algorithm.  It
 satisfies ``probably'' because it only failed in one out of ten cases,
-and it's ``approximate'' because it achieved low, but non-zero, error
+and its ``approximate'' because it achieved low, but non-zero, error
 on the remainder of the cases.
 
 This leads to the formal definition of an $(\ep,\de)$ PAC-learning
@@ -510,7 +510,7 @@ \section{Complexity of Infinite Hypothesis Spaces}
 dimension is the \emph{maximum} number of points for which you can
 always find such a classifier.
 
-\thinkaboutit{What is that labeling?  What is it's name?}
+\thinkaboutit{What is that labeling?  What is its name?}
 
 You can think of VC dimension as a game between you and an adversary.
 To play this game, \emph{you} choose $K$ unlabeled points however you