formating

Author: tenosel

README.md

@@ -11,15 +11,16 @@ It ends with applying gaussian mixture models (gmm) and the expectation-maximiza
 Implement functions to compute sample mean and the standard deviation.
 1. Use
 
-    $$ \hat{\mu} = \frac{1}{n} \sum_{i=1}^n x_i , $$
+$$ \hat{\mu} = \frac{1}{n} \sum_{i=1}^n x_i , $$
 
-    to calculate the mean.
+to calculate the mean.
 
 2. Use 
 
-    $$ \hat{\sigma} = \sqrt{\frac{1}{n-1} \sum_{i=1}^n (x_i - \hat{\mu})^2} $$
+$$ \hat{\sigma} = \sqrt{\frac{1}{n-1} \sum_{i=1}^n (x_i - \hat{\mu})^2} $$
+
+to compute the standard deviation. 
 
-    to compute the standard deviation. 
 $x_i \in \mathbb{R}$ for $i \in \{1, ... , n\}$ denotes individual sample elements, and $n \in \mathbb{N}$ the size of the sample.
 Don't use the pre-build functions np.mean() or np.std() to solve these tasks.