diff --git a/model.Rmd b/model.Rmd
index 27e1f57..b9ac9cb 100644
--- a/model.Rmd
+++ b/model.Rmd
@@ -65,18 +65,20 @@ The modelling chapters are even more opinionated than the rest of the book. I ap
 * *Statistical Modeling: A Fresh Approach* by Danny Kaplan,
   <http://www.mosaic-web.org/go/StatisticalModeling/>. This book provides 
   a gentle introduction to modelling, where you build your intuition,
-  mathematical tools, and R skills in parallel.
+  mathematical tools, and R skills in parallel. The book replaces a traditional
+  "introduction to statistics" course, providing a curriculum that is up-to-date 
+  and relevant to data science.
 
-* *An Introduction to  Statistical Learning* by Gareth James, Daniela Witten, 
+* *An Introduction to Statistical Learning* by Gareth James, Daniela Witten, 
   Trevor Hastie, and Robert Tibshirani, <http://www-bcf.usc.edu/~gareth/ISL/> 
   (available online for free). This book presents a family of modern modelling
-  techniques collectively known as statistical learning.
+  techniques collectively known as statistical learning.  For an even deeper
+  understanding of the math behind the models, read the classic 
+  *Elements of Statistical Learning* by Trevor Hastie, Robert Tibshirani, and
+  Jerome Friedman, <http://statweb.stanford.edu/~tibs/ElemStatLearn/> (also
+  available online for free).
 
 * *Applied Predictive Modeling* by Max Kuhn and Kjell Johnson, 
   <http://appliedpredictivemodeling.com>. This book is a companion to the 
   __caret__ package, and provides practical tools for dealing with real-life
   predictive modelling challenges.
-
-<!-- 
-For much of the insirpiration of the visualisations of these models, and extensions to more complex families, you might like "MODEL VIS PAPER"
--->