small change to project 1 text.

doc/Projects/2019/Project1/html/._Project1-bs000.html

@@ -159,7 +159,7 @@ MathJax.Hub.Config({
 <center><b>Department of Physics, University of Oslo, Norway</b></center>
 <br>
 <p>
-<center><h4>Sep 26, 2019</h4></center> <!-- date -->
+<center><h4>Oct 6, 2019</h4></center> <!-- date -->
 <br>
 <p>
 </div> <!-- end jumbotron -->
@@ -375,7 +375,7 @@ of data points, and possibly also your training and test data.
 <p>
 Try to make a figure similar to Fig. 2.11 of Hastie, Tibshirani, and
 Friedman, see the references below. You should include an analysis of the bias and variance for the test results. Figure 2.11 displays only the test and training MSEs while indicating regions of low/high bias and variance. You will most likely not get an
-equally smooth curve!
+equally smooth curve! Note also that when you calculate the bias, in all applications you don't know the function values \( f_i \). You would hence replace them with the actual data points \( y_i \).
 
 <h3 id="___sec4" class="anchor">Part d): Ridge Regression on the Franke function  with resampling </h3>
 

doc/Projects/2019/Project1/html/Project1-bs.html

@@ -159,7 +159,7 @@ MathJax.Hub.Config({
 <center><b>Department of Physics, University of Oslo, Norway</b></center>
 <br>
 <p>
-<center><h4>Sep 26, 2019</h4></center> <!-- date -->
+<center><h4>Oct 6, 2019</h4></center> <!-- date -->
 <br>
 <p>
 </div> <!-- end jumbotron -->
@@ -375,7 +375,7 @@ of data points, and possibly also your training and test data.
 <p>
 Try to make a figure similar to Fig. 2.11 of Hastie, Tibshirani, and
 Friedman, see the references below. You should include an analysis of the bias and variance for the test results. Figure 2.11 displays only the test and training MSEs while indicating regions of low/high bias and variance. You will most likely not get an
-equally smooth curve!
+equally smooth curve! Note also that when you calculate the bias, in all applications you don't know the function values \( f_i \). You would hence replace them with the actual data points \( y_i \).
 
 <h3 id="___sec4" class="anchor">Part d): Ridge Regression on the Franke function  with resampling </h3>
 

doc/Projects/2019/Project1/html/Project1.html

@@ -116,7 +116,7 @@ MathJax.Hub.Config({
 <center><b>Department of Physics, University of Oslo, Norway</b></center>
 <br>
 <p>
-<center><h4>Sep 26, 2019</h4></center> <!-- date -->
+<center><h4>Oct 6, 2019</h4></center> <!-- date -->
 <br>
 
 <h2 id="___sec0">Regression analysis and resampling methods  </h2>
@@ -330,7 +330,7 @@ of data points, and possibly also your training and test data.
 <p>
 Try to make a figure similar to Fig. 2.11 of Hastie, Tibshirani, and
 Friedman, see the references below. You should include an analysis of the bias and variance for the test results. Figure 2.11 displays only the test and training MSEs while indicating regions of low/high bias and variance. You will most likely not get an
-equally smooth curve!
+equally smooth curve! Note also that when you calculate the bias, in all applications you don't know the function values \( f_i \). You would hence replace them with the actual data points \( y_i \).
 
 <h3 id="___sec4">Part d): Ridge Regression on the Franke function  with resampling </h3>
 

doc/Projects/2019/Project1/pdf/Project1.p.tex

@@ -155,7 +155,7 @@ Project 1 on Machine Learning, deadline October 7, 2019
 
 % --- begin date ---
 \begin{center}
-Sep 26, 2019
+Oct 6, 2019
 \end{center}
 % --- end date ---
 
@@ -352,7 +352,7 @@ of data points, and possibly also your training and test data.
 
 Try to make a figure similar to Fig.~2.11 of Hastie, Tibshirani, and
 Friedman, see the references below. You should include an analysis of the bias and variance for the test results. Figure 2.11 displays only the test and training MSEs while indicating regions of low/high bias and variance. You will most likely not get an
-equally smooth curve!
+equally smooth curve! Note also that when you calculate the bias, in all applications you don't know the function values $f_i$. You would hence replace them with the actual data points $y_i$.
 
 \paragraph{Part d): Ridge Regression on the Franke function  with resampling.}
 Write your own code for the Ridge method, either using matrix

doc/Projects/2019/Project1/pdf/Project1.tex

@@ -125,7 +125,7 @@ Project 1 on Machine Learning, deadline October 7, 2019
 
 % --- begin date ---
 \begin{center}
-Sep 26, 2019
+Oct 6, 2019
 \end{center}
 % --- end date ---
 
@@ -322,7 +322,7 @@ of data points, and possibly also your training and test data.
 
 Try to make a figure similar to Fig.~2.11 of Hastie, Tibshirani, and
 Friedman, see the references below. You should include an analysis of the bias and variance for the test results. Figure 2.11 displays only the test and training MSEs while indicating regions of low/high bias and variance. You will most likely not get an
-equally smooth curve!
+equally smooth curve! Note also that when you calculate the bias, in all applications you don't know the function values $f_i$. You would hence replace them with the actual data points $y_i$.
 
 \paragraph{Part d): Ridge Regression on the Franke function  with resampling.}
 Write your own code for the Ridge method, either using matrix

doc/Projects/2019/Project1/pdf/Project1.tex~

@@ -125,7 +125,7 @@ Project 1 on Machine Learning, deadline October 7, 2019
 
 % --- begin date ---
 \begin{center}
-Sep 26, 2019
+Oct 6, 2019
 \end{center}
 % --- end date ---
 
@@ -322,7 +322,7 @@ of data points, and possibly also your training and test data.
 
 Try to make a figure similar to Fig.~2.11 of Hastie, Tibshirani, and
 Friedman, see the references below. You should include an analysis of the bias and variance for the test results. Figure 2.11 displays only the test and training MSEs while indicating regions of low/high bias and variance. You will most likely not get an
-equally smooth curve!
+equally smooth curve! Note also that when you calculate the bias, in all applications you don't know the function values $f_i$. You would hence replace them with the actual data points $y_i$.
 
 \paragraph{Part d): Ridge Regression on the Franke function  with resampling.}
 Write your own code for the Ridge method, either using matrix

doc/src/Projects/2019/Project1/Project1.do.txt

@@ -212,7 +212,7 @@ of data points, and possibly also your training and test data.
 
 Try to make a figure similar to Fig. 2.11 of Hastie, Tibshirani, and
 Friedman, see the references below. You should include an analysis of the bias and variance for the test results. Figure 2.11 displays only the test and training MSEs while indicating regions of low/high bias and variance. You will most likely not get an
-equally smooth curve!
+equally smooth curve! Note also that when you calculate the bias, in all applications you don't know the function values $f_i$. You would hence replace them with the actual data points $y_i$.
 
 === Part d): Ridge Regression on the Franke function  with resampling ===