diff --git a/doc/Projects/2020/Project2/html/._Project2-bs000.html b/doc/Projects/2020/Project2/html/._Project2-bs000.html
index 0b41c7ed5..e0b896d18 100644
--- a/doc/Projects/2020/Project2/html/._Project2-bs000.html
+++ b/doc/Projects/2020/Project2/html/._Project2-bs000.html
@@ -7,9 +7,9 @@ Automatically generated HTML file from DocOnce source
 <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
 <meta name="generator" content="DocOnce: https://github.com/hplgit/doconce/" />
 <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-<meta name="description" content="Project 2 on Machine Learning, deadline November 9">
+<meta name="description" content="Project 2 on Machine Learning, deadline November 13 (Midnight)">
 
-<title>Project 2 on Machine Learning, deadline November 9</title>
+<title>Project 2 on Machine Learning, deadline November 13 (Midnight)</title>
 
 <!-- Bootstrap style: bootstrap -->
 <link href="https://netdna.bootstrapcdn.com/bootstrap/3.1.1/css/bootstrap.min.css" rel="stylesheet">
@@ -107,7 +107,7 @@ MathJax.Hub.Config({
       <span class="icon-bar"></span>
       <span class="icon-bar"></span>
     </button>
-    <a class="navbar-brand" href="Project2-bs.html">Project 2 on Machine Learning, deadline November 9</a>
+    <a class="navbar-brand" href="Project2-bs.html">Project 2 on Machine Learning, deadline November 13 (Midnight)</a>
   </div>
 
   <div class="navbar-collapse collapse navbar-responsive-collapse">
@@ -144,7 +144,7 @@ MathJax.Hub.Config({
 
 
 <div class="jumbotron">
-<center><h1>Project 2 on Machine Learning, deadline November 9</h1></center>  <!-- document title -->
+<center><h1>Project 2 on Machine Learning, deadline November 13 (Midnight)</h1></center>  <!-- document title -->
 
 <p>
 <!-- author(s): <a href="http://www.uio.no/studier/emner/matnat/fys/FYS3155/index-eng.html" target="_self">Data Analysis and Machine Learning FYS-STK3155/FYS4155</a> -->
@@ -159,7 +159,7 @@ MathJax.Hub.Config({
 <center><b>Department of Physics, University of Oslo, Norway</b></center>
 <br>
 <p>
-<center><h4>Oct 7, 2020</h4></center> <!-- date -->
+<center><h4>Nov 4, 2020</h4></center> <!-- date -->
 <br>
 <p>
 </div> <!-- end jumbotron -->
diff --git a/doc/Projects/2020/Project2/html/Project2-bs.html b/doc/Projects/2020/Project2/html/Project2-bs.html
index 0b41c7ed5..e0b896d18 100644
--- a/doc/Projects/2020/Project2/html/Project2-bs.html
+++ b/doc/Projects/2020/Project2/html/Project2-bs.html
@@ -7,9 +7,9 @@ Automatically generated HTML file from DocOnce source
 <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
 <meta name="generator" content="DocOnce: https://github.com/hplgit/doconce/" />
 <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-<meta name="description" content="Project 2 on Machine Learning, deadline November 9">
+<meta name="description" content="Project 2 on Machine Learning, deadline November 13 (Midnight)">
 
-<title>Project 2 on Machine Learning, deadline November 9</title>
+<title>Project 2 on Machine Learning, deadline November 13 (Midnight)</title>
 
 <!-- Bootstrap style: bootstrap -->
 <link href="https://netdna.bootstrapcdn.com/bootstrap/3.1.1/css/bootstrap.min.css" rel="stylesheet">
@@ -107,7 +107,7 @@ MathJax.Hub.Config({
       <span class="icon-bar"></span>
       <span class="icon-bar"></span>
     </button>
-    <a class="navbar-brand" href="Project2-bs.html">Project 2 on Machine Learning, deadline November 9</a>
+    <a class="navbar-brand" href="Project2-bs.html">Project 2 on Machine Learning, deadline November 13 (Midnight)</a>
   </div>
 
   <div class="navbar-collapse collapse navbar-responsive-collapse">
@@ -144,7 +144,7 @@ MathJax.Hub.Config({
 
 
 <div class="jumbotron">
-<center><h1>Project 2 on Machine Learning, deadline November 9</h1></center>  <!-- document title -->
+<center><h1>Project 2 on Machine Learning, deadline November 13 (Midnight)</h1></center>  <!-- document title -->
 
 <p>
 <!-- author(s): <a href="http://www.uio.no/studier/emner/matnat/fys/FYS3155/index-eng.html" target="_self">Data Analysis and Machine Learning FYS-STK3155/FYS4155</a> -->
@@ -159,7 +159,7 @@ MathJax.Hub.Config({
 <center><b>Department of Physics, University of Oslo, Norway</b></center>
 <br>
 <p>
-<center><h4>Oct 7, 2020</h4></center> <!-- date -->
+<center><h4>Nov 4, 2020</h4></center> <!-- date -->
 <br>
 <p>
 </div> <!-- end jumbotron -->
diff --git a/doc/Projects/2020/Project2/html/Project2.html b/doc/Projects/2020/Project2/html/Project2.html
index 474c8a8ee..45d889a73 100644
--- a/doc/Projects/2020/Project2/html/Project2.html
+++ b/doc/Projects/2020/Project2/html/Project2.html
@@ -7,9 +7,9 @@ Automatically generated HTML file from DocOnce source
 <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
 <meta name="generator" content="DocOnce: https://github.com/hplgit/doconce/" />
 <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-<meta name="description" content="Project 2 on Machine Learning, deadline November 9">
+<meta name="description" content="Project 2 on Machine Learning, deadline November 13 (Midnight)">
 
-<title>Project 2 on Machine Learning, deadline November 9</title>
+<title>Project 2 on Machine Learning, deadline November 13 (Midnight)</title>
 
 
 <style type="text/css">
@@ -102,7 +102,7 @@ MathJax.Hub.Config({
 
 
 
-<center><h1>Project 2 on Machine Learning, deadline November 9</h1></center>  <!-- document title -->
+<center><h1>Project 2 on Machine Learning, deadline November 13 (Midnight)</h1></center>  <!-- document title -->
 
 <p>
 <!-- author(s): <a href="http://www.uio.no/studier/emner/matnat/fys/FYS3155/index-eng.html" target="_blank">Data Analysis and Machine Learning FYS-STK3155/FYS4155</a> -->
@@ -117,7 +117,7 @@ MathJax.Hub.Config({
 <center><b>Department of Physics, University of Oslo, Norway</b></center>
 <br>
 <p>
-<center><h4>Oct 7, 2020</h4></center> <!-- date -->
+<center><h4>Nov 4, 2020</h4></center> <!-- date -->
 <br>
 
 <h2 id="___sec0">Classification and Regression, from linear and logistic regression to neural networks  </h2>
diff --git a/doc/Projects/2020/Project2/ipynb/Project2.ipynb b/doc/Projects/2020/Project2/ipynb/Project2.ipynb
index 185c66e54..9ccc97fcd 100644
--- a/doc/Projects/2020/Project2/ipynb/Project2.ipynb
+++ b/doc/Projects/2020/Project2/ipynb/Project2.ipynb
@@ -4,13 +4,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<!-- dom:TITLE: Project 2 on Machine Learning, deadline November 9 -->\n",
-    "# Project 2 on Machine Learning, deadline November 9\n",
+    "<!-- dom:TITLE: Project 2 on Machine Learning, deadline November 13 (Midnight) -->\n",
+    "# Project 2 on Machine Learning, deadline November 13 (Midnight)\n",
     "<!-- dom:AUTHOR: [Data Analysis and Machine Learning FYS-STK3155/FYS4155](http://www.uio.no/studier/emner/matnat/fys/FYS3155/index-eng.html) at Department of Physics, University of Oslo, Norway -->\n",
     "<!-- Author: -->  \n",
     "**[Data Analysis and Machine Learning FYS-STK3155/FYS4155](http://www.uio.no/studier/emner/matnat/fys/FYS3155/index-eng.html)**, Department of Physics, University of Oslo, Norway\n",
     "\n",
-    "Date: **Oct 7, 2020**\n",
+    "Date: **Nov 4, 2020**\n",
     "\n",
     "Copyright 1999-2020, [Data Analysis and Machine Learning FYS-STK3155/FYS4155](http://www.uio.no/studier/emner/matnat/fys/FYS3155/index-eng.html). Released under CC Attribution-NonCommercial 4.0 license\n",
     "\n",
diff --git a/doc/Projects/2020/Project2/ipynb/ipynb-Project2-src.tar.gz b/doc/Projects/2020/Project2/ipynb/ipynb-Project2-src.tar.gz
index b2306dea7..b09822053 100644
Binary files a/doc/Projects/2020/Project2/ipynb/ipynb-Project2-src.tar.gz and b/doc/Projects/2020/Project2/ipynb/ipynb-Project2-src.tar.gz differ
diff --git a/doc/Projects/2020/Project2/pdf/Project2.p.tex b/doc/Projects/2020/Project2/pdf/Project2.p.tex
index ad702a856..6197acff4 100644
--- a/doc/Projects/2020/Project2/pdf/Project2.p.tex
+++ b/doc/Projects/2020/Project2/pdf/Project2.p.tex
@@ -129,7 +129,7 @@ final,                   % draft: marks overfull hboxes, figures with paths
 \begin{center}
 {\LARGE\bf
 \begin{spacing}{1.25}
-Project 2 on Machine Learning, deadline November 9
+Project 2 on Machine Learning, deadline November 13 (Midnight)
 \end{spacing}
 }
 \end{center}
@@ -149,7 +149,7 @@ Project 2 on Machine Learning, deadline November 9
 
 % --- begin date ---
 \begin{center}
-Oct 7, 2020
+Nov 4, 2020
 \end{center}
 % --- end date ---
 
diff --git a/doc/Projects/2020/Project2/pdf/Project2.pdf b/doc/Projects/2020/Project2/pdf/Project2.pdf
index d7ce5a55a..df174da4e 100644
Binary files a/doc/Projects/2020/Project2/pdf/Project2.pdf and b/doc/Projects/2020/Project2/pdf/Project2.pdf differ
diff --git a/doc/Projects/2020/Project2/pdf/Project2.tex b/doc/Projects/2020/Project2/pdf/Project2.tex
index 44473544e..e0c841617 100644
--- a/doc/Projects/2020/Project2/pdf/Project2.tex
+++ b/doc/Projects/2020/Project2/pdf/Project2.tex
@@ -103,7 +103,7 @@ final,                   % draft: marks overfull hboxes, figures with paths
 \begin{center}
 {\LARGE\bf
 \begin{spacing}{1.25}
-Project 2 on Machine Learning, deadline November 9
+Project 2 on Machine Learning, deadline November 13 (Midnight)
 \end{spacing}
 }
 \end{center}
@@ -123,7 +123,7 @@ Project 2 on Machine Learning, deadline November 9
 
 % --- begin date ---
 \begin{center}
-Oct 7, 2020
+Nov 4, 2020
 \end{center}
 % --- end date ---
 
diff --git a/doc/pub/week45/html/._week45-bs011.html b/doc/pub/week45/html/._week45-bs011.html
index d9e986c7a..ce7c3b94f 100644
--- a/doc/pub/week45/html/._week45-bs011.html
+++ b/doc/pub/week45/html/._week45-bs011.html
@@ -206,6 +206,7 @@ MathJax.Hub.Config({
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.svm</span> <span style="color: #008000; font-weight: bold">import</span> SVC
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.linear_model</span> <span style="color: #008000; font-weight: bold">import</span> LogisticRegression
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.tree</span> <span style="color: #008000; font-weight: bold">import</span> DecisionTreeClassifier
+<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.ensemble</span> <span style="color: #008000; font-weight: bold">import</span> BaggingClassifier
 
 <span style="color: #408080; font-style: italic"># Load the data</span>
 cancer <span style="color: #666666">=</span> load_breast_cancer()
diff --git a/doc/pub/week45/html/week45-reveal.html b/doc/pub/week45/html/week45-reveal.html
index 9dbb101c9..2604858f8 100644
--- a/doc/pub/week45/html/week45-reveal.html
+++ b/doc/pub/week45/html/week45-reveal.html
@@ -518,6 +518,7 @@ We will grow of forest of say \( B \) trees.
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.svm</span> <span style="color: #8B008B; font-weight: bold">import</span> SVC
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.linear_model</span> <span style="color: #8B008B; font-weight: bold">import</span> LogisticRegression
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.tree</span> <span style="color: #8B008B; font-weight: bold">import</span> DecisionTreeClassifier
+<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.ensemble</span> <span style="color: #8B008B; font-weight: bold">import</span> BaggingClassifier
 
 <span style="color: #228B22"># Load the data</span>
 cancer = load_breast_cancer()
diff --git a/doc/pub/week45/html/week45-solarized.html b/doc/pub/week45/html/week45-solarized.html
index e2662a58e..270a6f183 100644
--- a/doc/pub/week45/html/week45-solarized.html
+++ b/doc/pub/week45/html/week45-solarized.html
@@ -497,6 +497,7 @@ We will grow of forest of say \( B \) trees.
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.svm</span> <span style="color: #8B008B; font-weight: bold">import</span> SVC
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.linear_model</span> <span style="color: #8B008B; font-weight: bold">import</span> LogisticRegression
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.tree</span> <span style="color: #8B008B; font-weight: bold">import</span> DecisionTreeClassifier
+<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.ensemble</span> <span style="color: #8B008B; font-weight: bold">import</span> BaggingClassifier
 
 <span style="color: #228B22"># Load the data</span>
 cancer = load_breast_cancer()
diff --git a/doc/pub/week45/html/week45.html b/doc/pub/week45/html/week45.html
index 1d79dfcd2..ab66fb0d5 100644
--- a/doc/pub/week45/html/week45.html
+++ b/doc/pub/week45/html/week45.html
@@ -502,6 +502,7 @@ We will grow of forest of say \( B \) trees.
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.svm</span> <span style="color: #008000; font-weight: bold">import</span> SVC
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.linear_model</span> <span style="color: #008000; font-weight: bold">import</span> LogisticRegression
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.tree</span> <span style="color: #008000; font-weight: bold">import</span> DecisionTreeClassifier
+<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.ensemble</span> <span style="color: #008000; font-weight: bold">import</span> BaggingClassifier
 
 <span style="color: #408080; font-style: italic"># Load the data</span>
 cancer <span style="color: #666666">=</span> load_breast_cancer()
diff --git a/doc/pub/week45/ipynb/ipynb-week45-src.tar.gz b/doc/pub/week45/ipynb/ipynb-week45-src.tar.gz
index 54d7c65e1..340bd56c3 100644
Binary files a/doc/pub/week45/ipynb/ipynb-week45-src.tar.gz and b/doc/pub/week45/ipynb/ipynb-week45-src.tar.gz differ
diff --git a/doc/pub/week45/ipynb/week45.ipynb b/doc/pub/week45/ipynb/week45.ipynb
index 20f80412e..d4cdac36d 100644
--- a/doc/pub/week45/ipynb/week45.ipynb
+++ b/doc/pub/week45/ipynb/week45.ipynb
@@ -77,9 +77,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
@@ -133,10 +131,21 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x252 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "\n",
     "# Common imports\n",
@@ -175,10 +184,23 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LogisticRegression 0.864\n",
+      "RandomForestClassifier 0.872\n",
+      "SVC 0.888\n",
+      "VotingClassifier 0.896\n",
+      "LogisticRegression 0.864\n",
+      "RandomForestClassifier 0.872\n",
+      "SVC 0.888\n",
+      "VotingClassifier 0.912\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.datasets import make_moons\n",
@@ -234,10 +256,21 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "VotingClassifier(estimators=[('lr', LogisticRegression(random_state=42)),\n",
+       "                             ('rf', RandomForestClassifier(random_state=42)),\n",
+       "                             ('svc', SVC(random_state=42))])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.datasets import make_moons\n",
@@ -262,10 +295,19 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LogisticRegression 0.864\n",
+      "RandomForestClassifier 0.896\n",
+      "SVC 0.896\n",
+      "VotingClassifier 0.912\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.metrics import accuracy_score\n",
     "\n",
@@ -278,10 +320,22 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "VotingClassifier(estimators=[('lr', LogisticRegression(random_state=42)),\n",
+       "                             ('rf', RandomForestClassifier(random_state=42)),\n",
+       "                             ('svc', SVC(probability=True, random_state=42))],\n",
+       "                 voting='soft')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "log_clf = LogisticRegression(random_state=42)\n",
     "rnd_clf = RandomForestClassifier(random_state=42)\n",
@@ -296,10 +350,19 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LogisticRegression 0.864\n",
+      "RandomForestClassifier 0.896\n",
+      "SVC 0.896\n",
+      "VotingClassifier 0.92\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.metrics import accuracy_score\n",
     "\n",
@@ -386,10 +449,82 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(426, 30)\n",
+      "(143, 30)\n",
+      "Test set accuracy with Logistic Regression: 0.95\n",
+      "Test set accuracy with SVM: 0.63\n",
+      "Test set accuracy with Decision Trees: 0.87\n",
+      "Test set accuracy Logistic Regression with scaled data: 0.96\n",
+      "Test set accuracy SVM with scaled data: 0.96\n",
+      "Test set accuracy with Decision Trees and scaled data: 0.91\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/hjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.         0.73333333 0.93333333 1.         1.         0.92857143\n",
+      " 1.         0.92857143 0.92857143 0.92857143]\n",
+      "Test set accuracy with Random Forests and scaled data: 0.98\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
@@ -398,6 +533,7 @@
     "from sklearn.svm import SVC\n",
     "from sklearn.linear_model import LogisticRegression\n",
     "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.ensemble import BaggingClassifier\n",
     "\n",
     "# Load the data\n",
     "cancer = load_breast_cancer()\n",
@@ -468,9 +604,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "bag_clf = BaggingClassifier(\n",
@@ -481,10 +615,19 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9790209790209791"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "bag_clf.fit(X_train, y_train)\n",
     "y_pred = bag_clf.predict(X_test)\n",
@@ -1025,9 +1168,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.ensemble import AdaBoostClassifier\n",
@@ -1238,9 +1379,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -1301,9 +1440,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -1374,9 +1511,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -1439,9 +1574,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "\n",
@@ -1499,7 +1632,25 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 4
 }
diff --git a/doc/src/Projects/2020/Project2/Project2.do.txt b/doc/src/Projects/2020/Project2/Project2.do.txt
index d8fc91f51..bfc55d13f 100644
--- a/doc/src/Projects/2020/Project2/Project2.do.txt
+++ b/doc/src/Projects/2020/Project2/Project2.do.txt
@@ -1,4 +1,4 @@
-TITLE: Project 2 on Machine Learning, deadline November 9
+TITLE: Project 2 on Machine Learning, deadline November 13 (Midnight)
 AUTHOR: "Data Analysis and Machine Learning FYS-STK3155/FYS4155":"http://www.uio.no/studier/emner/matnat/fys/FYS3155/index-eng.html" {copyright, 1999-present|CC BY-NC} at Department of Physics, University of Oslo, Norway
 DATE: today
 
diff --git a/doc/src/week45/week45.do.txt b/doc/src/week45/week45.do.txt
index 47a1157e1..ee33c888d 100644
--- a/doc/src/week45/week45.do.txt
+++ b/doc/src/week45/week45.do.txt
@@ -314,6 +314,7 @@ from sklearn.datasets import load_breast_cancer
 from sklearn.svm import SVC
 from sklearn.linear_model import LogisticRegression
 from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import BaggingClassifier
 
 # Load the data
 cancer = load_breast_cancer()