diff --git a/doc/Projects/2019/Project1/html/._Project1-bs000.html b/doc/Projects/2019/Project1/html/._Project1-bs000.html
index 0e1c861c4..794b53b80 100644
--- a/doc/Projects/2019/Project1/html/._Project1-bs000.html
+++ b/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>
 
diff --git a/doc/Projects/2019/Project1/html/Project1-bs.html b/doc/Projects/2019/Project1/html/Project1-bs.html
index 0e1c861c4..794b53b80 100644
--- a/doc/Projects/2019/Project1/html/Project1-bs.html
+++ b/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>
 
diff --git a/doc/Projects/2019/Project1/html/Project1.html b/doc/Projects/2019/Project1/html/Project1.html
index f84e992cd..0475cefeb 100644
--- a/doc/Projects/2019/Project1/html/Project1.html
+++ b/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>
 
diff --git a/doc/Projects/2019/Project1/ipynb/ipynb-Project1-src.tar.gz b/doc/Projects/2019/Project1/ipynb/ipynb-Project1-src.tar.gz
index 44c25d08c..bf754f5bf 100644
Binary files a/doc/Projects/2019/Project1/ipynb/ipynb-Project1-src.tar.gz and b/doc/Projects/2019/Project1/ipynb/ipynb-Project1-src.tar.gz differ
diff --git a/doc/Projects/2019/Project1/pdf/Project1.p.tex b/doc/Projects/2019/Project1/pdf/Project1.p.tex
index 6c4d050b5..9247a7113 100644
--- a/doc/Projects/2019/Project1/pdf/Project1.p.tex
+++ b/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
diff --git a/doc/Projects/2019/Project1/pdf/Project1.pdf b/doc/Projects/2019/Project1/pdf/Project1.pdf
index 0380e19af..a23573085 100644
Binary files a/doc/Projects/2019/Project1/pdf/Project1.pdf and b/doc/Projects/2019/Project1/pdf/Project1.pdf differ
diff --git a/doc/Projects/2019/Project1/pdf/Project1.tex b/doc/Projects/2019/Project1/pdf/Project1.tex
index d24675702..9ffce0c2c 100644
--- a/doc/Projects/2019/Project1/pdf/Project1.tex
+++ b/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
diff --git a/doc/Projects/2019/Project1/pdf/Project1.tex~ b/doc/Projects/2019/Project1/pdf/Project1.tex~
index 9253df903..1f8ba0784 100644
--- a/doc/Projects/2019/Project1/pdf/Project1.tex~
+++ b/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
diff --git a/doc/src/Projects/2019/Project1/Project1.do.txt b/doc/src/Projects/2019/Project1/Project1.do.txt
index b1accc914..ed050a033 100644
--- a/doc/src/Projects/2019/Project1/Project1.do.txt
+++ b/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 ===