adding update to regression slides

doc/src/Regression/Regression.do.txt

@@ -974,10 +974,14 @@ The examples we have looked at so far are cases where we normally can
 invert the matrix $\bm{X}^T\bm{X}$. Using a polynomial expansion as we
 did both for the masses and the fitting of the equation of state,
 leads to row vectors of the design matrix which are essentially
-orthogonal due to the polynomial character of our model. This may
+orthogonal due to the polynomial character of our model. Obtaining the inverse of the design matrix is then often done via a so-called LU, QR or Cholesky decomposition. 
+More material to come here. 
+
+
+This may
 however not the be case in general and a standard matrix inversion
-algorithm based on say LU decomposition may lead to singularities. We will see an example of this below when we try to fit
-the coupling constant of the widely used Ising model. 
+algorithm based on say LU, QR or Cholesky decomposition may lead to singularities. We will see examples of this below.
+
 There is however a way to partially circumvent this problem and also gain some insight about the ordinary least squares approach. 
 
 This is given by the _Singular Value Decomposition_ algorithm, perhaps
@@ -987,6 +991,9 @@ inversion algorithm. Thereafter we dive into the math of the SVD.
 
 !eblock
 
+
+# todo: change model here.
+
 !split
 =====  The Ising model =====