correcting typos

doc/pub/week34/html/._week34-bs065.html

@@ -503,7 +503,7 @@ that is the degree of a given polynomial.
 
 <p>The aim is to reproduce Figure 2.11 of <a href="https://github.com/CompPhysics/MLErasmus/blob/master/doc/Textbooks/elementsstat.pdf" target="_self">Hastie et al</a>.</p>
 
-<p>Our data is defined by \( x\in [-3,3] \) with a total of for example \( 100 \) data points. You should try to vary \( n \) in your analysis.</p>
+<p>Our data is defined by \( x\in [-3,3] \) with a total of for example \( n=100 \) data points. You should try to vary the number of data points \( n \) in your analysis.</p>
 
 <!-- code=python (!bc pycod) typeset with pygments style "default" -->
 <div class="cell border-box-sizing code_cell rendered">
@@ -544,7 +544,8 @@ Write a first code which sets up a design matrix \( X \) defined by a fifth-orde
 <!-- --- begin subexercise --- -->
 <p>
 <b>b)</b>
-Perform an ordinary least squares fitting and compute the means squared error for the training data and the test data.
+Write thereafter (using either <b>scikit-learn</b> or your matrix inversion code using for example <b>numpy</b>)
+and perform an ordinary least squares fitting and compute the mean squared error for the training data and the test data. These calculations should apply to a model given by a fifth-order polynomial.
 </p>
 
 <!-- --- end subexercise --- -->

doc/pub/week34/html/week34-reveal.html

@@ -3966,7 +3966,7 @@ that is the degree of a given polynomial.
 
 <p>The aim is to reproduce Figure 2.11 of <a href="https://github.com/CompPhysics/MLErasmus/blob/master/doc/Textbooks/elementsstat.pdf" target="_blank">Hastie et al</a>.</p>
 
-<p>Our data is defined by \( x\in [-3,3] \) with a total of for example \( 100 \) data points. You should try to vary \( n \) in your analysis.</p>
+<p>Our data is defined by \( x\in [-3,3] \) with a total of for example \( n=100 \) data points. You should try to vary the number of data points \( n \) in your analysis.</p>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
 <div class="cell border-box-sizing code_cell rendered">
@@ -4007,7 +4007,8 @@ Write a first code which sets up a design matrix \( X \) defined by a fifth-orde
 <!-- --- begin subexercise --- -->
 <p>
 <b>b)</b>
-Perform an ordinary least squares fitting and compute the means squared error for the training data and the test data.
+Write thereafter (using either <b>scikit-learn</b> or your matrix inversion code using for example <b>numpy</b>)
+and perform an ordinary least squares fitting and compute the mean squared error for the training data and the test data. These calculations should apply to a model given by a fifth-order polynomial.
 </p>
 
 <!-- --- end subexercise --- -->

doc/pub/week34/html/week34-solarized.html

@@ -3831,7 +3831,7 @@ that is the degree of a given polynomial.
 
 <p>The aim is to reproduce Figure 2.11 of <a href="https://github.com/CompPhysics/MLErasmus/blob/master/doc/Textbooks/elementsstat.pdf" target="_blank">Hastie et al</a>.</p>
 
-<p>Our data is defined by \( x\in [-3,3] \) with a total of for example \( 100 \) data points. You should try to vary \( n \) in your analysis.</p>
+<p>Our data is defined by \( x\in [-3,3] \) with a total of for example \( n=100 \) data points. You should try to vary the number of data points \( n \) in your analysis.</p>
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
 <div class="cell border-box-sizing code_cell rendered">
@@ -3872,7 +3872,8 @@ Write a first code which sets up a design matrix \( X \) defined by a fifth-orde
 <!-- --- begin subexercise --- -->
 <p>
 <b>b)</b>
-Perform an ordinary least squares fitting and compute the means squared error for the training data and the test data.
+Write thereafter (using either <b>scikit-learn</b> or your matrix inversion code using for example <b>numpy</b>)
+and perform an ordinary least squares fitting and compute the mean squared error for the training data and the test data. These calculations should apply to a model given by a fifth-order polynomial.
 </p>
 
 <!-- --- end subexercise --- -->

doc/pub/week34/html/week34.html

@@ -3908,7 +3908,7 @@ that is the degree of a given polynomial.
 
 <p>The aim is to reproduce Figure 2.11 of <a href="https://github.com/CompPhysics/MLErasmus/blob/master/doc/Textbooks/elementsstat.pdf" target="_blank">Hastie et al</a>.</p>
 
-<p>Our data is defined by \( x\in [-3,3] \) with a total of for example \( 100 \) data points. You should try to vary \( n \) in your analysis.</p>
+<p>Our data is defined by \( x\in [-3,3] \) with a total of for example \( n=100 \) data points. You should try to vary the number of data points \( n \) in your analysis.</p>
 
 <!-- code=python (!bc pycod) typeset with pygments style "default" -->
 <div class="cell border-box-sizing code_cell rendered">
@@ -3949,7 +3949,8 @@ Write a first code which sets up a design matrix \( X \) defined by a fifth-orde
 <!-- --- begin subexercise --- -->
 <p>
 <b>b)</b>
-Perform an ordinary least squares fitting and compute the means squared error for the training data and the test data.
+Write thereafter (using either <b>scikit-learn</b> or your matrix inversion code using for example <b>numpy</b>)
+and perform an ordinary least squares fitting and compute the mean squared error for the training data and the test data. These calculations should apply to a model given by a fifth-order polynomial.
 </p>
 
 <!-- --- end subexercise --- -->

doc/src/week34/week34.do.txt

@@ -2584,7 +2584,7 @@ that is the degree of a given polynomial.
 
 The aim is to reproduce Figure 2.11 of "Hastie et al":"https://github.com/CompPhysics/MLErasmus/blob/master/doc/Textbooks/elementsstat.pdf".
 
-Our data is defined by $x\in [-3,3]$ with a total of for example $100$ data points. You should try to vary $n$ in your analysis.
+Our data is defined by $x\in [-3,3]$ with a total of for example $n=100$ data points. You should try to vary the number of data points $n$ in your analysis.
 !bc pycod
 np.random.seed()
 n = 100
@@ -2598,7 +2598,8 @@ Write a first code which sets up a design matrix $X$ defined by a fifth-order po
 !esubex
 
 !bsubex
-Perform an ordinary least squares fitting and compute the means squared error for the training data and the test data.
+Write thereafter (using either _scikit-learn_ or your matrix inversion code using for example _numpy_)
+and perform an ordinary least squares fitting and compute the mean squared error for the training data and the test data. These calculations should apply to a model given by a fifth-order polynomial.
 !esubex
 
 !bsubex