--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/day1/data/pos.txt	Wed Oct 28 20:33:17 2009 +0530
@@ -0,0 +1,41 @@
+0.  0.
+0.25     0.47775
+0.5    0.931
+0.75     1.35975
+1.     1.764
+1.25     2.14375
+1.5    2.499
+1.75     2.82975
+2.     3.136
+2.25     3.41775
+2.5    3.675
+2.75     3.90775
+3.     4.116
+3.25     4.29975
+3.5    4.459
+3.75     4.59375
+4.     4.704
+4.25     4.78975
+4.5    4.851
+4.75     4.88775
+5.   4.9
+5.25     4.88775
+5.5    4.851
+5.75     4.78975
+6.     4.704
+6.25     4.59375
+6.5    4.459
+6.75     4.29975
+7.     4.116
+7.25     3.90775
+7.5    3.675
+7.75     3.41775
+8.     3.136
+8.25     2.82975
+8.5    2.499
+8.75     2.14375
+9.     1.764
+9.25     1.35975
+9.5    0.931
+9.75     0.47775
+10.   0.

--- a/day1/session3.tex	Wed Oct 28 20:33:01 2009 +0530
+++ b/day1/session3.tex	Wed Oct 28 20:33:17 2009 +0530
@@ -588,18 +588,19 @@
 \item A is also called a Van der Monde matrix
 \item It can be generated using \typ{vander}
 \end{itemize}
-Van der Monde matrix of order M
+\begin{lstlisting}
+In []: A = vander(L, 2)
+\end{lstlisting}
+Gives the required Van der Monde matrix
 \begin{equation*}
   \begin{bmatrix}
-  l_1^{M-1} & \ldots & l_1 & 1 \\
-  l_2^{M-1} & \ldots &l_2 & 1 \\
-  \vdots & \ldots & \vdots & \vdots\\
-  l_N^{M-1} & \ldots & l_N & 1 \\
+    l_1 & 1 \\
+    l_2 & 1 \\
+    \vdots & \vdots\\
+    l_N & 1 \\
   \end{bmatrix}
 \end{equation*}
-\begin{lstlisting}
-In []: A = vander(L,2)
-\end{lstlisting}
+
 \end{frame}
 
 \begin{frame}[fragile]

--- a/day1/session5.tex	Wed Oct 28 20:33:01 2009 +0530
+++ b/day1/session5.tex	Wed Oct 28 20:33:17 2009 +0530
@@ -129,7 +129,6 @@
 \begin{frame}[fragile]
 \frametitle{Interpolation}
 \begin{itemize}
-\item Let us begin with interpolation
 \item Let's use the L and T arrays and interpolate this data to obtain data at new points
 \end{itemize}
 \begin{lstlisting}

--- a/day1/session6.tex	Wed Oct 28 20:33:01 2009 +0530
+++ b/day1/session6.tex	Wed Oct 28 20:33:17 2009 +0530
@@ -285,7 +285,7 @@
 %% \end{frame}
 
 \begin{frame}[fragile]
-\frametitle{Newton Raphson Method}
+\frametitle{Newton-Raphson Method}
 \begin{enumerate}
 \item Start with an initial guess of x for the root
 \item $\Delta x = -f(x)/f^{'}(x)$
@@ -295,7 +295,7 @@
 \end{frame}
 
 %% \begin{frame}[fragile]
-%% \frametitle{Newton Raphson \dots}
+%% \frametitle{Newton-Raphson \dots}
 %% \begin{lstlisting}
 %% In []: def our_df(x):
 %%  ....:     return -sin(x)-2*x
@@ -310,10 +310,10 @@
 %% \end{frame}
 
 \begin{frame}[fragile]
-\frametitle{Newton Raphson \ldots}
+\frametitle{Newton-Raphson \ldots}
 \begin{itemize}
 \item What if $f^{'}(x) = 0$?
-\item Can you write a better version of the Newton Raphson?
+\item Can you write a better version of the Newton-Raphson?
 \item What if the differential is not easy to calculate?
 \item Look at Secant Method
 \end{itemize}
@@ -368,7 +368,7 @@
   \item Finding Roots
     \begin{itemize}
     \item Estimating Interval
-    \item Newton Raphson
+    \item Newton-Raphson
     \item Scipy methods
     \end{itemize}
   \end{itemize}