diff --git a/exercises-pdf/ic_nested_resampling.pdf b/exercises-pdf/ic_nested_resampling.pdf
index d6996677c..9f7e4dde4 100644
Binary files a/exercises-pdf/ic_nested_resampling.pdf and b/exercises-pdf/ic_nested_resampling.pdf differ
diff --git a/exercises/nested-resampling/ex_rnw/ex_recap_nested_resampling.Rnw b/exercises/nested-resampling/ex_rnw/ex_recap_nested_resampling.Rnw
index af187443c..b8c25c271 100644
--- a/exercises/nested-resampling/ex_rnw/ex_recap_nested_resampling.Rnw
+++ b/exercises/nested-resampling/ex_rnw/ex_recap_nested_resampling.Rnw
@@ -14,5 +14,5 @@ Answer the following questions:
     \end{itemize}
   \item[2)] In which order (e.g., "A-B-C") can the three goals be tackled?
   \item[3)] Write down a pseudo-algorithm for carrying out all three steps (in a sensible order as derived in 2))
-  \item[4)] Assume the number of hidden layers is $\in{\{1,2,3,4,5\}}$, the number of trees is $\in{\{10,50,100,200\}}$ and the maximal depth is $\in{\{2,3,4,5\}}$. Use 3-fold cross-validation as outer resampling and 4-fold cross-validaion as inner resampling. Compute the total number of model trainings carried out in 3).
+  \item[4)] Assume the number of hidden layers is $\in{\{1,2,3,4,5\}}$, the number of trees is $\in{\{10,50,100,200\}}$ and the maximal depth is $\in{\{2,3,4,5\}}$. Use 3-fold cross-validation as outer resampling and 4-fold cross-validation as inner resampling. Use grid search and consider all possible hyperparameter combinations. Compute the total number of model trainings carried out in 3).
 \end{itemize}