diff --git a/14-Adaptive-Filtering.ipynb b/14-Adaptive-Filtering.ipynb
index 50e0f2d..348959c 100644
--- a/14-Adaptive-Filtering.ipynb
+++ b/14-Adaptive-Filtering.ipynb
@@ -1336,7 +1336,7 @@
     "\n",
     "$$ \\tilde{\\mathbf P} = \\alpha^2\\mathbf{FPF}^\\mathsf T + \\mathbf Q$$\n",
     "\n",
-    "where $\\alpha > 1.0$. If $\\alpha == 1$ then we get the normal Kalman filter performance. $\\alpha$ is an attribute of the `KalmanFilger` class; its value defaults to 1 so the filter acts like a Kalman filter unless $\\alpha$ is assigned a value other than 1. There is no hard and fast rule for choosing $\\alpha$, but it is typically very close to 1, such as 1.01. You will need to make many runs with either simulated or real data to determine a value that responds to maneuvers without causing the estimate to become too noisy due to overly weighting the noisy measurement.\n",
+    "where $\\alpha > 1.0$. If $\\alpha == 1$ then we get the normal Kalman filter performance. $\\alpha$ is an attribute of the `KalmanFilter` class; its value defaults to 1 so the filter acts like a Kalman filter unless $\\alpha$ is assigned a value other than 1. There is no hard and fast rule for choosing $\\alpha$, but it is typically very close to 1, such as 1.01. You will need to make many runs with either simulated or real data to determine a value that responds to maneuvers without causing the estimate to become too noisy due to overly weighting the noisy measurement.\n",
     "\n",
     "Why does this work? If we increase the estimate error covariance the filter becomes more uncertain about it's estimate, hence it gives more weight to the measurement. \n",
     "\n",
@@ -2271,7 +2271,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -2285,7 +2285,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.5.2"
   }
  },
  "nbformat": 4,