diff --git a/docs/rmd/ml-doubledebiased.Rmd b/docs/rmd/ml-doubledebiased.Rmd
index e8b05ec..5b94285 100644
--- a/docs/rmd/ml-doubledebiased.Rmd
+++ b/docs/rmd/ml-doubledebiased.Rmd
@@ -603,6 +603,6 @@ $$ \frac{\hat{\mu}(x) - \mu(x)}{\hat{\sigma}_n(x)} \leadsto N(0,1) $$
 
 <!-- --- -->
 
-# Bibliography
+# References
 
 <!-- --- -->
diff --git a/docs/rmd/ml-intro.Rmd b/docs/rmd/ml-intro.Rmd
index 293cfa5..e1e5524 100644
--- a/docs/rmd/ml-intro.Rmd
+++ b/docs/rmd/ml-intro.Rmd
@@ -61,9 +61,9 @@ if (exists("slides") && slides) {
 # Introduction
 
 <div class=notes>
-These notes will examine the incorportion of machine learning
+These notes will examine the incorporation of machine learning
 methods in classic econometric techniques for estimating causal
-effects. More specifally, we will focus on estimating treatment
+effects. More specifically, we will focus on estimating treatment
 effects using matching and instrumental variables. In these
 estimators (and many others) there is a low-dimensional parameter
 of interest, such as the average treatment effect, but estimating