correcting project

doc/src/Projects/2020/Project1/Project1.do.txt

@@ -9,7 +9,7 @@ DATE: today
 The main aim of this project is to study in more detail various
 regression methods, including the Ordinary Least Squares (OLS) method,
 Ridge regression and finally Lasso regression.
-The methods are in turn combined with resampling techniques.
+The methods are in turn combined with resampling techniques like the bootstrap method and cross validation.
 
 We will first study how to fit polynomials to a specific
 two-dimensional function called "Franke's
@@ -33,8 +33,8 @@ f(x,y) &= \frac{3}{4}\exp{\left(-\frac{(9x-2)^2}{4} - \frac{(9y-2)^2}{4}\right)}
 The function will be defined for $x,y\in [0,1]$.  Our first step will
 be to perform an OLS regression analysis of this function, trying out
 a polynomial fit with an $x$ and $y$ dependence of the form $[x, y,
-x^2, y^2, xy, \dots]$. We will also include cross-validation (or bootstrap) as
-resampling technique.  As in homeworks 1 and 2, we can use a uniform
+x^2, y^2, xy, \dots]$. We will also include bootstrap first as
+a resampling technique.  After that we will include the cross-validation technique. As in homeworks 1 and 2, we can use a uniform
 distribution to set up the arrays of values for $x$ and $y$, or as in
 the example below just a set of fixed 
 values for $x$ and $y$ with a given step
@@ -100,7 +100,7 @@ plt.show()
 We will generate our own dataset for a function
 $\mathrm{FrankeFunction}(x,y)$ with $x,y \in [0,1]$. The function
 $f(x,y)$ is the Franke function. You should explore also the addition
-an added stochastic noise to this function using the normal
+of an added stochastic noise to this function using the normal
 distribution $\cal{N}(0,1)$.
 
 Write your own code (using either a matrix inversion or a singular