added files

doc/src/week43/make.sh

@@ -44,7 +44,7 @@ system doconce split_html $html.html --method=space10
 # Bootstrap style
 html=${name}-bs
 system doconce format html $name --html_style=bootstrap --pygments_html_style=default --html_admon=bootstrap_panel --html_output=$html $opt
-system doconce split_html $html.html --method=split --pagination --nav_button=bottom
+#system doconce split_html $html.html --method=split --pagination --nav_button=bottom
 
 # IPython notebook
 system doconce format ipynb $name $opt

doc/src/week44/week44.do.txt

@@ -55,7 +55,7 @@ Assume, we are given $n$ data points and we wish to split the data into $K < n$
 different categories, or clusters. We label each cluster by an integer
 
 !bt
-\[ k\in\{1, \cdots, K \}$.
+\[ k\in\{1, \cdots, K \}.
 \]
 !et
 
@@ -151,7 +151,7 @@ Now we have all the pieces necessary to formally revisit the $k$-means algorithm
 The $k$-means clustering algorithm goes as follows 
 
 o For a given cluster assignment $C$, and $k$ cluster means
-  $\left{m_1, \cdots, m_k\right}$. We minimize the total cluster variance with respect to
+  $\left\{m_1, \cdots, m_k\right\}$. We minimize the total cluster variance with respect to
   the cluster means $\{m_k\}$ yielding the means of the currently assigned
   clusters.
 o Given a current set of $k$ means $\{m_k\}$ the total cluster variance is