maskani-moh
diff --git a/‎doc/tutorial/statistical_inference/supervised_learning.rst‎
Lines changed: 3 additions & 3 deletions b/‎doc/tutorial/statistical_inference/supervised_learning.rst‎
Lines changed: 3 additions & 3 deletions
@@ -109,21 +109,21 @@ The curse of dimensionality
 
 For an estimator to be effective, you need the distance between neighboring
 points to be less than some value :math:`d`, which depends on the problem.
-In one dimension, this requires on average :math:`n ~ 1/d` points.
+In one dimension, this requires on average :math:`n \sim 1/d` points.
 In the context of the above :math:`k`-NN example, if the data is described by
 just one feature with values ranging from 0 to 1 and with :math:`n` training
 observations, then new data will be no further away than :math:`1/n`.
 Therefore, the nearest neighbor decision rule will be efficient as soon as
 :math:`1/n` is small compared to the scale of between-class feature variations.
 
-If the number of features is :math:`p`, you now require :math:`n ~ 1/d^p`
+If the number of features is :math:`p`, you now require :math:`n \sim 1/d^p`
 points.  Let's say that we require 10 points in one dimension: now :math:`10^p`
 points are required in :math:`p` dimensions to pave the :math:`[0, 1]` space.
 As :math:`p` becomes large, the number of training points required for a good
 estimator grows exponentially.
 
 For example, if each point is just a single number (8 bytes), then an
-effective :math:`k`-NN estimator in a paltry :math:`p~20` dimensions would
+effective :math:`k`-NN estimator in a paltry :math:`p \sim 20` dimensions would
 require more training data than the current estimated size of the entire
 internet (±1000 Exabytes or so).