diff --git a/table_of_contents.ipynb b/table_of_contents.ipynb
index d3eecd5..cc7feb3 100644
--- a/table_of_contents.ipynb
+++ b/table_of_contents.ipynb
@@ -79,7 +79,7 @@
     "Kalman filters are recursive, and thus very suitable for real time filtering. However, they work extremely well for post-processing data. After all, Kalman filters are predictor-correctors, and it is easier to predict the past than the future! We discuss some common approaches.\n",
     "\n",
     "\n",
-    "[**Chapter 14: Adaptive Filtering**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/13-Adaptive-Filtering.ipynb)\n",
+    "[**Chapter 14: Adaptive Filtering**](http://nbviewer.ipython.org/urls/raw.github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/master/14-Adaptive-Filtering.ipynb)\n",
     "    \n",
     "Kalman filters assume a single process model, but manuevering targets typically need to be described by several different process models. Adaptive filtering uses several techniques to allow the Kalman filter to adapt to the changing behavior of the target.\n",
     "\n",