diff --git a/doc/pub/How2ReadData/html/How2ReadData-bs.html b/doc/pub/How2ReadData/html/How2ReadData-bs.html
index b3f391ef5..674f33701 100644
--- a/doc/pub/How2ReadData/html/How2ReadData-bs.html
+++ b/doc/pub/How2ReadData/html/How2ReadData-bs.html
@@ -166,7 +166,7 @@ MathJax.Hub.Config({
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Aug 14, 2019</h4></center> <!-- date -->
+<center><h4>Dec 20, 2019</h4></center> <!-- date -->
 <br>
 <p>
 </div> <!-- end jumbotron -->
@@ -1074,6 +1074,15 @@ $$
 \epsilon_{\mathrm{relative}}= \frac{\vert \hat{y} -\hat{\tilde{y}}\vert}{\vert \hat{y}\vert}.
 $$
 
+<p>
+The squared cost function results in an arithmetic mean-unbiased
+estimator, and the absolute-value cost function results in a
+median-unbiased estimator (in the one-dimensional case, and a
+geometric median-unbiased estimator for the multi-dimensional
+case). The squared cost function has the disadvantage that it has the tendency
+to be dominated by outliers.
+
+<p>
 We can modify easily the above Python code and plot the relative error instead
 <p>
 
@@ -1179,7 +1188,7 @@ $$
 \text{MAE}(\hat{y}, \hat{\tilde{y}}) = \frac{1}{n} \sum_{i=0}^{n-1} \left| y_i - \tilde{y}_i \right|.
 $$
 
-Finally we present the 
+We present the 
 squared logarithmic (quadratic) error
 $$
 \text{MSLE}(\hat{y}, \hat{\tilde{y}}) = \frac{1}{n} \sum_{i=0}^{n - 1} (\log_e (1 + y_i) - \log_e (1 + \tilde{y}_i) )^2,
@@ -1191,6 +1200,11 @@ estimate is best to use when targets having exponential growth, such
 as population counts, average sales of a commodity over a span of
 years etc.
 
+<p>
+Finally, another cost function is the Huber cost function used in robust regression.
+It is less sensitive to outliers in data than the squared error cost function.
+A variant for classification is also sometimes used, a quantity we will meet later.
+
 <p>
 We will discuss in more
 detail these and other functions in the various lectures.  We conclude this part with another example. Instead of 
diff --git a/doc/pub/How2ReadData/html/How2ReadData-reveal.html b/doc/pub/How2ReadData/html/How2ReadData-reveal.html
index 5353224f2..dbf50c07d 100644
--- a/doc/pub/How2ReadData/html/How2ReadData-reveal.html
+++ b/doc/pub/How2ReadData/html/How2ReadData-reveal.html
@@ -148,7 +148,7 @@ MathJax.Hub.Config({
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>&nbsp;<br>
-<center><h4>Aug 14, 2019</h4></center> <!-- date -->
+<center><h4>Dec 20, 2019</h4></center> <!-- date -->
 <br>
 
 <h2 id="___sec0">Introduction </h2>
@@ -1089,6 +1089,15 @@ $$
 $$
 <p>&nbsp;<br>
 
+<p>
+The squared cost function results in an arithmetic mean-unbiased
+estimator, and the absolute-value cost function results in a
+median-unbiased estimator (in the one-dimensional case, and a
+geometric median-unbiased estimator for the multi-dimensional
+case). The squared cost function has the disadvantage that it has the tendency
+to be dominated by outliers.
+
+<p>
 We can modify easily the above Python code and plot the relative error instead
 <p>
 
@@ -1202,7 +1211,7 @@ $$
 $$
 <p>&nbsp;<br>
 
-Finally we present the 
+We present the 
 squared logarithmic (quadratic) error
 <p>&nbsp;<br>
 $$
@@ -1216,6 +1225,11 @@ estimate is best to use when targets having exponential growth, such
 as population counts, average sales of a commodity over a span of
 years etc.
 
+<p>
+Finally, another cost function is the Huber cost function used in robust regression.
+It is less sensitive to outliers in data than the squared error cost function.
+A variant for classification is also sometimes used, a quantity we will meet later.
+
 <p>
 We will discuss in more
 detail these and other functions in the various lectures.  We conclude this part with another example. Instead of 
diff --git a/doc/pub/How2ReadData/html/How2ReadData-solarized.html b/doc/pub/How2ReadData/html/How2ReadData-solarized.html
index 5b0ab6a7b..5feadf7ba 100644
--- a/doc/pub/How2ReadData/html/How2ReadData-solarized.html
+++ b/doc/pub/How2ReadData/html/How2ReadData-solarized.html
@@ -135,7 +135,7 @@ MathJax.Hub.Config({
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Aug 14, 2019</h4></center> <!-- date -->
+<center><h4>Dec 20, 2019</h4></center> <!-- date -->
 <br>
 
 <h2 id="___sec0">Introduction </h2>
@@ -1034,6 +1034,15 @@ $$
 \epsilon_{\mathrm{relative}}= \frac{\vert \hat{y} -\hat{\tilde{y}}\vert}{\vert \hat{y}\vert}.
 $$
 
+<p>
+The squared cost function results in an arithmetic mean-unbiased
+estimator, and the absolute-value cost function results in a
+median-unbiased estimator (in the one-dimensional case, and a
+geometric median-unbiased estimator for the multi-dimensional
+case). The squared cost function has the disadvantage that it has the tendency
+to be dominated by outliers.
+
+<p>
 We can modify easily the above Python code and plot the relative error instead
 <p>
 
@@ -1139,7 +1148,7 @@ $$
 \text{MAE}(\hat{y}, \hat{\tilde{y}}) = \frac{1}{n} \sum_{i=0}^{n-1} \left| y_i - \tilde{y}_i \right|.
 $$
 
-Finally we present the 
+We present the 
 squared logarithmic (quadratic) error
 $$
 \text{MSLE}(\hat{y}, \hat{\tilde{y}}) = \frac{1}{n} \sum_{i=0}^{n - 1} (\log_e (1 + y_i) - \log_e (1 + \tilde{y}_i) )^2,
@@ -1151,6 +1160,11 @@ estimate is best to use when targets having exponential growth, such
 as population counts, average sales of a commodity over a span of
 years etc.
 
+<p>
+Finally, another cost function is the Huber cost function used in robust regression.
+It is less sensitive to outliers in data than the squared error cost function.
+A variant for classification is also sometimes used, a quantity we will meet later.
+
 <p>
 We will discuss in more
 detail these and other functions in the various lectures.  We conclude this part with another example. Instead of 
diff --git a/doc/pub/How2ReadData/html/How2ReadData.html b/doc/pub/How2ReadData/html/How2ReadData.html
index 24aea122f..154c9bac9 100644
--- a/doc/pub/How2ReadData/html/How2ReadData.html
+++ b/doc/pub/How2ReadData/html/How2ReadData.html
@@ -140,7 +140,7 @@ MathJax.Hub.Config({
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Aug 14, 2019</h4></center> <!-- date -->
+<center><h4>Dec 20, 2019</h4></center> <!-- date -->
 <br>
 
 <h2 id="___sec0">Introduction </h2>
@@ -1039,6 +1039,15 @@ $$
 \epsilon_{\mathrm{relative}}= \frac{\vert \hat{y} -\hat{\tilde{y}}\vert}{\vert \hat{y}\vert}.
 $$
 
+<p>
+The squared cost function results in an arithmetic mean-unbiased
+estimator, and the absolute-value cost function results in a
+median-unbiased estimator (in the one-dimensional case, and a
+geometric median-unbiased estimator for the multi-dimensional
+case). The squared cost function has the disadvantage that it has the tendency
+to be dominated by outliers.
+
+<p>
 We can modify easily the above Python code and plot the relative error instead
 <p>
 
@@ -1144,7 +1153,7 @@ $$
 \text{MAE}(\hat{y}, \hat{\tilde{y}}) = \frac{1}{n} \sum_{i=0}^{n-1} \left| y_i - \tilde{y}_i \right|.
 $$
 
-Finally we present the 
+We present the 
 squared logarithmic (quadratic) error
 $$
 \text{MSLE}(\hat{y}, \hat{\tilde{y}}) = \frac{1}{n} \sum_{i=0}^{n - 1} (\log_e (1 + y_i) - \log_e (1 + \tilde{y}_i) )^2,
@@ -1156,6 +1165,11 @@ estimate is best to use when targets having exponential growth, such
 as population counts, average sales of a commodity over a span of
 years etc.
 
+<p>
+Finally, another cost function is the Huber cost function used in robust regression.
+It is less sensitive to outliers in data than the squared error cost function.
+A variant for classification is also sometimes used, a quantity we will meet later.
+
 <p>
 We will discuss in more
 detail these and other functions in the various lectures.  We conclude this part with another example. Instead of 
diff --git a/doc/pub/How2ReadData/ipynb/How2ReadData.ipynb b/doc/pub/How2ReadData/ipynb/How2ReadData.ipynb
index a141115c9..bf0bdf435 100644
--- a/doc/pub/How2ReadData/ipynb/How2ReadData.ipynb
+++ b/doc/pub/How2ReadData/ipynb/How2ReadData.ipynb
@@ -10,7 +10,7 @@
     "<!-- Author: -->  \n",
     "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
     "\n",
-    "Date: **Aug 14, 2019**\n",
+    "Date: **Dec 20, 2019**\n",
     "\n",
     "Copyright 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
     "\n",
@@ -427,7 +427,9 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "import numpy as np"
@@ -443,7 +445,9 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "n = 10\n",
@@ -462,7 +466,9 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -481,7 +487,9 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -505,7 +513,9 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -527,7 +537,9 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -545,7 +557,9 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -563,7 +577,9 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -585,7 +601,9 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -603,7 +621,9 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -622,7 +642,9 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -641,7 +663,9 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -661,7 +685,9 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -681,7 +707,9 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -765,7 +793,9 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "# Importing various packages\n",
@@ -788,7 +818,9 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
@@ -834,81 +866,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>First Name</th>\n",
-       "      <th>Last Name</th>\n",
-       "      <th>Place of birth</th>\n",
-       "      <th>Date of Birth T.A.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Frodo</td>\n",
-       "      <td>Baggins</td>\n",
-       "      <td>Shire</td>\n",
-       "      <td>2968</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Bilbo</td>\n",
-       "      <td>Baggins</td>\n",
-       "      <td>Shire</td>\n",
-       "      <td>2890</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Aragorn II</td>\n",
-       "      <td>Elessar</td>\n",
-       "      <td>Eriador</td>\n",
-       "      <td>2931</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Samwise</td>\n",
-       "      <td>Gamgee</td>\n",
-       "      <td>Shire</td>\n",
-       "      <td>2980</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   First Name Last Name Place of birth  Date of Birth T.A.\n",
-       "0       Frodo   Baggins          Shire                2968\n",
-       "1       Bilbo   Baggins          Shire                2890\n",
-       "2  Aragorn II   Elessar        Eriador                2931\n",
-       "3     Samwise    Gamgee          Shire                2980"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "from IPython.display import display\n",
@@ -933,81 +895,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>First Name</th>\n",
-       "      <th>Last Name</th>\n",
-       "      <th>Place of birth</th>\n",
-       "      <th>Date of Birth T.A.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Frodo</th>\n",
-       "      <td>Frodo</td>\n",
-       "      <td>Baggins</td>\n",
-       "      <td>Shire</td>\n",
-       "      <td>2968</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Bilbo</th>\n",
-       "      <td>Bilbo</td>\n",
-       "      <td>Baggins</td>\n",
-       "      <td>Shire</td>\n",
-       "      <td>2890</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Aragorn</th>\n",
-       "      <td>Aragorn II</td>\n",
-       "      <td>Elessar</td>\n",
-       "      <td>Eriador</td>\n",
-       "      <td>2931</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Sam</th>\n",
-       "      <td>Samwise</td>\n",
-       "      <td>Gamgee</td>\n",
-       "      <td>Shire</td>\n",
-       "      <td>2980</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         First Name Last Name Place of birth  Date of Birth T.A.\n",
-       "Frodo         Frodo   Baggins          Shire                2968\n",
-       "Bilbo         Bilbo   Baggins          Shire                2890\n",
-       "Aragorn  Aragorn II   Elessar        Eriador                2931\n",
-       "Sam         Samwise    Gamgee          Shire                2980"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
    "source": [
     "data_pandas = pd.DataFrame(data,index=['Frodo','Bilbo','Aragorn','Sam'])\n",
     "display(data_pandas)"
@@ -1022,23 +914,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "First Name            Aragorn II\n",
-       "Last Name                Elessar\n",
-       "Place of birth           Eriador\n",
-       "Date of Birth T.A.          2931\n",
-       "Name: Aragorn, dtype: object"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
    "source": [
     "display(data_pandas.loc['Aragorn'])"
    ]
@@ -1052,89 +932,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>First Name</th>\n",
-       "      <th>Last Name</th>\n",
-       "      <th>Place of birth</th>\n",
-       "      <th>Date of Birth T.A.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Frodo</th>\n",
-       "      <td>Frodo</td>\n",
-       "      <td>Baggins</td>\n",
-       "      <td>Shire</td>\n",
-       "      <td>2968</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Bilbo</th>\n",
-       "      <td>Bilbo</td>\n",
-       "      <td>Baggins</td>\n",
-       "      <td>Shire</td>\n",
-       "      <td>2890</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Aragorn</th>\n",
-       "      <td>Aragorn II</td>\n",
-       "      <td>Elessar</td>\n",
-       "      <td>Eriador</td>\n",
-       "      <td>2931</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Sam</th>\n",
-       "      <td>Samwise</td>\n",
-       "      <td>Gamgee</td>\n",
-       "      <td>Shire</td>\n",
-       "      <td>2980</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Pippin</th>\n",
-       "      <td>Peregrin</td>\n",
-       "      <td>Took</td>\n",
-       "      <td>Shire</td>\n",
-       "      <td>2990</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         First Name Last Name Place of birth  Date of Birth T.A.\n",
-       "Frodo         Frodo   Baggins          Shire                2968\n",
-       "Bilbo         Bilbo   Baggins          Shire                2890\n",
-       "Aragorn  Aragorn II   Elessar        Eriador                2931\n",
-       "Sam         Samwise    Gamgee          Shire                2980\n",
-       "Pippin     Peregrin      Took          Shire                2990"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
    "source": [
     "new_hobbit = {'First Name': [\"Peregrin\"],\n",
     "              'Last Name': [\"Took\"],\n",
@@ -1155,288 +957,11 @@
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-       "      <td>3.003620</td>\n",
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-       "      <td>0.127812</td>\n",
-       "      <td>2.603636</td>\n",
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -1462,187 +987,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
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-      "None\n",
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\n",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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Nc2daAAAAn+FW2Jo/f7769OmjwYMHa8uWLZozZ45eeeWVctsNHz5cEyZMcGcqAAAAn+TWPltbt25V9+7dJUkJCQnaunWry+127dqlP/3pT1q4cKFSUlLcmRIAAMCnVLqyNWbMGF24cKHc+MSJE5Wdna3g4GBJUkhIiC5duqSSkhLVqfPDu50yZYq6dOmiwsJCDR8+XG+88YZiYmIqLS4srL7q1Amoai/XFBnZwCPb+JK0Csat0qfV+/txH9fq60QV76Mmu1atvtSHK75ef1VZpc+K3luuR03+XVzPe0tN7qMiNbG/SsPWihUrKrwuIiJC+fn5atiwoWw2mxo1alQuaElSly5dJEn16tVThw4dlJKSUqWwlZNTUOk2VZWVlXfN6yMjG1S6jVVYvU+r9Hd1Hzf6/PSV30Vl/flKH67w3lI71eTfxfW8t9TkPirizf4qCm9u7bM1YMAApaamqlmzZkpJSdGAAQMkSWVlZTp79qyioqK0fft2FRcX67bbbpMkHT9+XNHR0e5MC1jOa0lzvV0CAMAQt8LWU089pT/+8Y86duyYMjMz9eyzz0qSDh06pKlTp2r9+vUKDw/XkiVLtH//fp0/f1533nmnevbs6ZHiAQAAajq3wlZoaKhmzZpVbrxDhw5av369JCkuLk6vvvqqO9MAAIBrqOg4Yk9s+aSaK4ErHEEeAADAIMIWAACAQZyuBz4rdvkq5+Xa9I0vAIBvYWULAADAIMIWAACAQXyMCAAANHZaordLsCxWtgAAAAwibAEAABhE2AIAADCIfbYAALAozrtaM7CyBQAAYBBhCwAAwCDCFgAAgEGELQAAAIMIWwAAAAYRtgAAAAwibAEAABjEcbYAANcldvkq5+XIyAbKysrzXjGAD2BlCwAAwCDCFgAAgEGELQAAAIMIWwAAAAYRtgAAAAwibAEAABhE2AIAADCI42wBqDHGTkv0dgkA4HGsbAEAABhE2AIAADCIsAUAAGAQYQsAAMAgwhYAAIBBhC0AAACDCFsAAAAGEbYAAAAMImwBAAAYRNgCAAAwyK2wVVZWpnfffVd9+vRRWlpahdtt27ZNM2fO1KuvvqolS5a4MyUAAIBPcevciAcPHlTXrl1Vr169CrcpLCzUjBkz9PHHHyswMFATJkzQ9u3b1adPH3emBgAAtdhrSXO9XUKVubWy1bFjR3Xo0OGa2+zZs0dRUVEKDAyUJCUkJCg5OdmdaQEAAHxGpStbY8aM0YULF8qNT5w4UbfffnulE2RnZys4ONj5c0hIiLKzs6tUXFhYfdWpE1ClbSsTGdnAI9v4koo+2LVan1dYta8rrtXfiRu4TU3jS7VeL6v19uN+rNZfxTvFVJ0v/U58qdarVbXua21XXb1XGrZWrFjh1gQRERHKz893/myz2RQREVGl2+bkFLg199WysvKueX1kZINKt7EKK/Zp9cfvRvvzld+JlR8/K/Z2dT9W7M8TfOV34suPX1Xqrqw/T/deUXgz9m3EzMxMSVK3bt10+vRp2e12SVJKSooSExNNTQsAAFCjuBW2Ll26pKVLlyovL09r167Vnj17JEkXL17UyJEjVVRUpHr16mnmzJmaNWuWFixYoLi4OHaOBwAAtYZb30Zs1KiRxo0bp3Hjxv1gPDw8XJ9//rnz5379+qlfv37uTAUAAOCTOKgpAACAQW6tbKFmi12+ynnZl3eChLU8/PIWj97fymlJHr0/APA0whYAS9u/f5+WLl2skpIS9ep1qyTJbrfLbrfr66/36M03VykgoOqHmNmwYb1++tNENWjgm1+XB1D9CFsALK1jx87q3r2HCgsLNWbM45KkoqIi7dq1QxMnPiU/P7/rur8NG9are/cehC0AVUbYAlCrlJSUaNmyJerRo6d++cthevXVN5SdfUF//OPLiouLV926gUpO3qy//OWveu21RWrTpp0yM49r0KAhKiq6rLNnz2jt2ncUExOje++9z9vtAPABhC3AAlp2f8HbJdR4KSm7tGjRPDkcDvn5+al//wF69901kqTOnbvopz8doKKiyxo3bpLuvfcXSk1N0XffXdLw4ffJbi/SpUuX1LJljJo2baYRI36jZs2ivNwRAF9B2AJQKyQk9NT48ZPlcDiUmen6BEcxMa0lSe3atVerVq119uwZPfXUeIWGhmnChCers1wAFsKhHwDUKn5+fmrZMqbC667IyEjXz342UK+/vkI9e/bW2rVvS5ICAgLkcDiUkZGu0tLSaqkZgG9jZQtAtXJ1qAaThyY5eHC/9u5NVXFxsbZs+UxJSXdIkrZt+0Lnzp3Vhx++r7vvHqa9e1OVkZGuNm3aKj6+owoLC7V27Wtq1aq1Tp7M1D33/FySdOutffTWW6tUUlKi6dNnGKkZgLUQtgBYWnx8Ry1evKzceN++/dW3b3/nzz/epmvX7uratXu5240c+VvPFwnA0vgYEQAAwCDCFgAAgEGELQAAAIMIWwAAAAYRtgAAAAzi24gAqtUTW6Z69P5eS5rr0fsDrgdnb0BVELYA1Aoffvg3pacfVlhYuE6fPqXGjSM1duyEapt/7949WrToFY0f/6QSEnpW27wAvI+wBcDy8vNtWr58mdav/1R+fn4qKSnRggXVuyLWtWs3tW3bvlrnBFAzELYAWF7duoFyOBx69901uuuuIQoNDdUzz0xXSUmJFi+ep7CwcNlsNrVvH6tBg+5WaWmplixZqEaNGslut+u7777T009P07FjR/Xuu28pOrqljh8/ppEjf6vw8AjNnPm8HI4yxcS0Vnp6mu688y4NGzZckjRr1izl5RUoKqq5zp8/7+XfBABvIGwBsLzAwEC9+uqbWrNmle6/f5Wio2P04INjdPbsGRUXl+ihhx6Vw+HQqFH36dZb+2jr1n+puLhYo0c/Ikn66KMPJUkvvfSinnzyGcXHd9S33+7Tyy//XsuWrdT99z+oN954TZMnP62cnBxNmvR/NWzYcG3b9oWOHTuml15aIEn6/POtXvoNAPAmwhaAWqFNm7b63e9+r9LSUm3d+i89//wzuuuuIcrOvqC//GWVc5vs7GwdOZKuFi1aOG87ZMi9kqQjRw4rKqq5JKlFixZKT09zbhMd3VKSFBYWpoKCAknS0aNH1KpVK+c2V24LoHbh0A8ALO/MmdN66aUXJUkBAQG67bZEBQYGqWnTZoqOjtYDD4zWAw+M1s9+dpeaNWumdu3a69SpU87bf/jh3yRJ7drF6tSpk5KkzMxMtW8f69zGz8+v3LytWrXR0aNHnT+fPn2q3DYArI+VLQDVytWhGiIjGygrK8/YnMHBIfruu0tavHiegoNDdObMaY0dO0F33z1MS5cu1ooVbyggIECSNGDA/9GQIfdoyZKFWrHiDZWWlqpNm7aSpGnTfqe33/5v7dz5lU6cOK5nn/2d7Ha7Nm7coCNHDuvgwf3KyDgim82m5OTNGjAgSXv37tTLL/9eN9/cRJJDGzduUHx8B9WvH2ysXwA1C2ELgOU1bNhQL700z+V1EyY8WW4sICBAkyZNKTfeqlVrTZ8+o9z4c8/951hL8fEdNXjwUOfPM2bMcAbJhx9+7LprB+D7+BgRAADAIMIWAACAQXyMCABAFYydlujtEuCjWNkCAAAwiLAFAABgEB8jAqhWaY+MLj/mxv3FLl/lxq0BwDzCFgBLGzfuEXXs2FnffXdJW7du0dCh35+z8JNPPtZNN92kv/1tfbnb/OEP/0/33fcrxcbGX/O+CwrytWjRPJWVlen552eaKB+ABRC2AFja3XcP0913D1NGRrpSUnZp/PjJkqTWrdvoz3/+k8vbPPfcCy6PCP9j9esHa+DAwfrnPz/yaM0ArIWwBcDS7r57WIXjK1a8oeXLl+nYsQzVrx+s6dNnKD39sBYufEWDBw9V//4DNHPm85IcatmylVJSduqRR8YqKChIH3zwnjp1ukV5ed9Vb0MAfA5hC0CtlZubq5EjH1D9+sF64IERunQpV+3atVf37j0kfX/k+fvvf1Cvv75Ykyc/rezsCyorK9OYMQ/oz39eo4iIxlq//kPl5OR4uRMANRlhC0CtFRYW5jxHYaNGoSooKFCjRqHltouJaS1JiohorJyci7p8+bIiIhpLkqKimuvrr/dUX9HVzNW5LAFcHw79AKDWqsp+WT/erlGjUAUFBenChQuSpNOnTxmpDYB1uLWyVVZWprVr12rRokVavXq1YmNjXW43YsQIBQUFSZL8/f21evVqd6YF4MNcHaohMrKB82TNJhQVXdY//vGBbDabPvponYYMuUfr138om82m5OTNatiwkc6dO6uPP/6HkpLu0N69qcrISFdCQk9t3LhBR44cVnLyZiUm3i5/f39Nnz5Dc+b8XvHxHXXxYraOHDmsvXv3qGvXbsZ6AOC73ApbBw8eVNeuXVWvXr1rbvfTn/5UEyZMcGcqALhhQUE3afLkZzR58jPOsaFD79XQofc6f37vvX84Ly9evMx5+bnnXih3f3369FOfPv0MVQvAatwKWx07dqzSdmlpaXrzzTdVVFSkW265RYmJie5MCwAA4DMqDVtjxoxx7ptwtYkTJ+r222+v0iSPPvqounTpotLSUo0aNUrBwcHq1atXpbcLC6uvOnUCqjRHZSIjG3hkG19Gf76N/nyXlXuTrNdfRWc0sFqfV/hqX1Wt+1rbVVfvlYatFStWuD1Jly5dJEkBAQHq2bOnvvrqqyqFrZycArfnvqKy/UFM7zPibfTn2+jPd1m5N8n6/V3Nin368uNXlbor68/TvVcU3ox9GzEzM1OSdOTIEb333nvO8ePHjys6OtrUtAAAADWKW/tsXbp0SWvWrFFeXp7Wrl2rIUOGqFu3brp48aJGjhypzz77TCEhIdq6davOnz8vm82mpk2baujQoZ6qHwAAoEZzK2w1atRI48aN07hx434wHh4ers8//1yS1KRJEy1ZssSdaQBYyOsvJ3v0/sZOS/To/QGAp3EEeQCWtn//Pi1dulglJSXq1etWSZLdbpfdbtekSVPcvv+UlF1q0KCB2reP07lzZ7V48Ty1adNOY8Y87vZ9A7AGwhYAS+vYsbO6d++hwsJCZwAqKirSrl07PHL/qam71axZlNq3j1OTJk3Vr99tOnPmtEfuG4A1ELYA1ColJSVatmyJJk2aoj//+U8qKSmRw+FQ3bp19dBDj+qzzzbqj398SZ98kqz9+/dp7tw/aOLEp9SuXaxmznxekkMtW7ZSSspODRw4WKmpu5WenqYzZ07r/vtHS5KOHs3QggVzlZZ2UOPHP6FOnXp4tWcA3kXYAlArpKTs0qJF8+RwOOTn56evvtquAwe+1dy5CyVJU6ZM1I4dX+qOOwZq2bLv9zPt2LGz2rf//jRkDRs21P33P6jXX1+syZOfVnb2BZWVleny5ctq1ixKgwf/54s/devW1ZNPTtXBg/u1Zs2f9fvfE7aA2oywBaBWSEjoqfHjJ8vhcCgz84S++GKroqKaO69v0aKF0tPT1Lv3T655PzExrSVJERGNK9ymRYvvD2/TqFGo8vPzPVA9AF9G2AJQq/j5+allyxi1axer1NTdzvHMzEz173+bJKl+/frKz7cpODhE586dLXf7qwUEBMjhcOjChSwFBPCWCtRE3v7WMu8MAKqVqzc9k0exPnhwv/buTVVxcbG2bPlMSUl3SJJ69/6Jvv32Gy1btkQOh0OdO9+iXr2+X9X6+c9HaMGCV9Sp0y3y8/PXxo0bFBsbr40bN+jIkcNKTt6sxMTvT1fWrVuC/vrXNUpJ2alHHx2n//mffysvL08nT2Zq48YNOnXqlHbt2qGePXsb6Q9AzefncDgc3i6iIjf65vvwy1vKja2clnTN2/jyKQuqgv58G/35Liv3Jlmzv7RHRrscj12+qlrrqA6+8vjdyN91qfr7q/bT9QAAAICwBQAAYBRhCwAAwCB2kAcA4CpX75vlK/s0oWZjZQsAAMAgVrYAVKsTqS+WH3Pj/lp2f8GNWwOAeYQtAJa2f/8+LV26WCUlJerV61ZJkt1ul91u19df79Gbb65SQECAMjLS9dZbqxUd3VJ16tRRUFCQRowYKUnatu0LLVgwV4sXL1OzZlHebAeADyJsAbC0jh07q3v3HiosLNSYMY9Lkor/dVSGAAAJkklEQVSKirRr1w5NnPiU84jwn3++VQkJPTVkyD0qKSnRr3893Bm2+vbtr7ff/m+v9QDUdlU5plZNRtgCUKuUlJRo2bIl6tGjp375y2F69dU3lJ+fr507v1KDBg1kt9vVqFGobLY8rVjxhmJiWumOOwZKkjZsWK+zZ8/ozJnTmjNnvoKDQ7zcDQBfYMmw5esJGIDnpaTs0qJF8+RwOOTn56f+/Qfo3XfXSJLatWuv7t17qFmzKA0ePFSS9Prri50rYVd06dJVY8Y8rvnz52jnzq+cp+wBgGuxZNgCgB9LSOip8eMny+FwKDPzxnbJb948WpLUqFGoCgoKPFkeAAvj0A8AahU/Pz+1bBlT6Xb+/v5yOBw6fDjtB7cFgOvFyhaAauXqUA0mDxx58OB+7d2bquLiYm3Z8pmSku6Q9P03DM+dO6sPP3xfAwfepb17U5WRka6mTZspIaGn+vbtryVLFkqScnMv6ty5s/r443/orruGOLft06e/wsLCjNQNwDr8HA6Hw9tFVKS6z9Rt5aME059voz/fZeXeJPrzdfTn+flc4WNEAAAAgwhbAAAABhG2AAAADCJsAQAAGETYAgAAMIiwBQAAYBBhCwAAwCDCFgAAgEGELQAAAINq9BHkAQAAfB0rWwAAAAYRtgAAAAwibAEAABhE2AIAADCIsAUAAGAQYQsAAMAgwhYAAIBBhK1agsOpATCptLTU2yUYlZeX5+0SjLL63whv91fHq7PXAHa7Xdu2bVNWVpbsdrtGjRrl7ZI86vz580pPT1ffvn3lcDjk5+fn7ZI85sKFC8rMzFRKSoqCg4P161//2tsledT58+d18uRJffPNN8rMzNTkyZMVEhLi7bI8pri4WL/73e8UFxenhx56yNvleNypU6e0c+dObd++XZcvX9aiRYu8XZJHlZaWaseOHTp27Ji2b9+u8ePHKzY21ttlecyV95adO3fq8OHD6tWrl55++mlvl+URNptNu3fv1smTJ3X06FHLvbfYbDbt3btXmZmZys/P1+jRoxUQEODVmmr9yta//vUvpaenKyEhQXv27NE777wjm83m7bI8Ii8vT0uWLNGzzz6r3NxcSwWtc+fOafbs2Tp58qT69eunDz/8UB999JG3y/Ko5557zhmUL126pLfffltlZWXeLstj0tLSdOrUKWVkZCg3N9fb5XjclClTdOTIEf3mN7/R7NmzvV2Oxx04cEA5OTlKSkrS1KlTFRUV5e2SPObcuXN69NFH9c0332jQoEFKTEzUgAEDvF2Wx6xYsUJpaWnq3bu3cnJy9M4776ioqMjbZXnM6tWrdfToUfXq1UsHDx7UypUr9d1333m1ploftt566y21a9dObdu21bBhw/Ttt99q165d3i7LI06ePKl77rlHt9xyi3bv3i3J+0upnjR8+HANHDhQ8fHx6tixo9LT071dkkcNGzZMgwYNUvv27dWsWTPZ7Xb5+/v+S/bKczAlJUU9evSQn5+fjh8/LkmWCpPDhg1T3759VVBQoC+++EIZGRneLsmj3nnnHTkcDh0/flxbt261VH9hYWGaN2+e/uu//kv9+/eXw+FQVlaWt8vymE2bNql169Zq37692rRpI0kKCgqyxN8Hu92uL7/8UvHx8Wrbtq3uuOMOpaam6ptvvvFqXb7/zu2G0tJSdejQQYcPH5YkxcbGqlGjRtq3b5+XK/OMuLg4de7cWS1atNCXX34pyTph6+abb9Ztt92mwMBASVLLli0VExPj5ao865577lHDhg316aef6sCBA2rbtq3sdru3y3Kbn5+fDh06pIiICI0YMUJ+fn7at2+fsrOzLREmr2jSpIm2bNmiiIgIlZWVadasWTpy5Ii3y/KIsrIyRUVFadOmTWrSpImio6O1cuVKrVu3ztuleURgYKA6deokScrPz1dYWJhatGjh5ao8Z+jQodq0aZPefPNNHT9+XGVlZcrKyrLEpx/FxcVq27attm3bJklq2rSpMjMzdejQIa/WZZ13thvg5+enpk2bOv9HFhYWpoYNGzr/gPs6f39/1a1bV507d9aBAwecY1Zw9ZtCenq6jh07puHDhys/P9+LVXleWVmZoqOjNWXKFG3evFnLli3zdkkecfHiRWVkZCgtLU3Hjx/Xpk2blJKS4u2yPKp///4aO3as4uLidOeddyo+Pl6ffvqpt8vyiCuvv/T0dMXExKh///7q1auXvvzyS8v8h+4Kf39/bd++XV26dPF2KR4zevRotW3bVo0bN9bjjz+uixcvav78+ZZ47IKDg3XnnXdq7969+uSTT7Rv3z717t1bwcHBXq3LGn95b5C/v7969eqlQ4cO6dKlSwoMDNSpU6ecy6pW4O/vr06dOqm0tFQ5OTneLseI/fv36/HHH1deXp62bNmi4uJib5fkMXa7XfHx8YqPj1ePHj1UXFxsiW99ZWRkqGnTpgoPD1diYqKio6MVExNjiTf7K4KCghQUFCRJqlOnjgoLCxUREeHlqjzDz89Pffv2VVBQkGw2m/z9/VVcXKx27dqppKTE2+V5VGBgoJo0aaLs7Gxvl+IxWVlZeu+99/Tzn/9cbdu2VYcOHRQZGWmZ/6z27dtXs2fP1s0336xRo0YpICBAcXFxXq2p1n8b8ZZbblFCQoJWrlyp0NBQhYSEqHXr1t4uy6NKSkqUm5urqVOnatKkSercubO3S/KYzZs3a8WKFfr3v/+tI0eO6LbbblNJSYnq1q3r7dLclpeXp02bNqmwsFAhISHatWuXhg4d6vVv1XjCyJEjnasjn3zyifbu3atNmzYpPDxcjRs39nJ1npGfn6/33ntPpaWlCg0NVVFRkaVee927d1eXLl20fPly3XzzzcrIyNCwYcMs8dq72saNGxUdHa2IiAjLfKP7pptuUrdu3bRgwQJFR0dr165dGjhwoKW+kZiZmamjR4/q9OnTioqKUnR0tFfr8XNY6b+SN8hutys9PV2lpaXq2LGjAgICLPOistvt+uyzz3TmzBl169ZNCQkJlujriuXLlyszM1P33Xef4uPjLfdGv3nzZp05c0b9+/dXdHS0JYLW1Ww2m7KyshQdHa06daz3f7/PP/9cmZmZuvXWW9WqVSvLPX75+flKT09XYGCgYmNjLfXeeUVGRoYcDofatm3r7VI8KiMjQ19//bU6deqkNm3aWO65+eWXX2rXrl0aNGiQ2rRp4/VdaAhbAAAABtXqfbYAAABMI2wBAAAYRNgCAAAwiLAFAABgEGELAADAIMIWAACAQYQtAAAAg/4/LeVtQQ7pZfIAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
    "source": [
     "df.columns = ['First', 'Second', 'Third', 'Fourth', 'Fifth']\n",
     "df.index = np.arange(10)\n",
@@ -1674,7 +1023,9 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "b = np.arange(16).reshape((4,4))\n",
@@ -1790,20 +1141,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x396 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
    "source": [
     "# Importing various packages\n",
     "import numpy as np\n",
@@ -1811,7 +1153,7 @@
     "from sklearn.linear_model import LinearRegression\n",
     "\n",
     "x = np.random.rand(100,1)\n",
-    "y = 2*x+0.01*np.random.randn(100,1)\n",
+    "y = 2*x+np.random.randn(100,1)\n",
     "linreg = LinearRegression()\n",
     "linreg.fit(x,y)\n",
     "xnew = np.array([[0],[1]])\n",
@@ -1918,32 +1260,30 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "The squared cost function results in an arithmetic mean-unbiased\n",
+    "estimator, and the absolute-value cost function results in a\n",
+    "median-unbiased estimator (in the one-dimensional case, and a\n",
+    "geometric median-unbiased estimator for the multi-dimensional\n",
+    "case). The squared cost function has the disadvantage that it has the tendency\n",
+    "to be dominated by outliers.\n",
+    "\n",
     "We can modify easily the above Python code and plot the relative error instead"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x396 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "from sklearn.linear_model import LinearRegression\n",
     "\n",
     "x = np.random.rand(100,1)\n",
-    "y = 5*x+np.random.randn(100,1)\n",
+    "y = 5*x+0.01*np.random.randn(100,1)\n",
     "linreg = LinearRegression()\n",
     "linreg.fit(x,y)\n",
     "ypredict = linreg.predict(x)\n",
@@ -1976,34 +1316,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The intercept alpha: \n",
-      " [2.]\n",
-      "Coefficient beta : \n",
-      " [[5.]]\n",
-      "Mean squared error: 0.00\n",
-      "Variance score: 1.00\n",
-      "Mean squared log error: 0.00\n",
-      "Mean absolute error: 0.00\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x396 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
    "source": [
     "import numpy as np \n",
     "import matplotlib.pyplot as plt \n",
@@ -2011,7 +1328,7 @@
     "from sklearn.metrics import mean_squared_error, r2_score, mean_squared_log_error, mean_absolute_error\n",
     "\n",
     "x = np.random.rand(100,1)\n",
-    "y = 2.0+ 5*x#+0.05*np.random.randn(100,1)\n",
+    "y = 2.0+ 5*x+0.5*np.random.randn(100,1)\n",
     "linreg = LinearRegression()\n",
     "linreg.fit(x,y)\n",
     "ypredict = linreg.predict(x)\n",
@@ -2117,7 +1434,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Finally we present the \n",
+    "We present the \n",
     "squared logarithmic (quadratic) error"
    ]
   },
@@ -2139,6 +1456,13 @@
     "as population counts, average sales of a commodity over a span of\n",
     "years etc. \n",
     "\n",
+    "\n",
+    "Finally, another cost function is the Huber cost function used in robust regression.\n",
+    "It is less sensitive to outliers in data than the squared error cost function.\n",
+    "A variant for classification is also sometimes used, a quantity we will meet later. \n",
+    "\n",
+    "\n",
+    "\n",
     "We will discuss in more\n",
     "detail these and other functions in the various lectures.  We conclude this part with another example. Instead of \n",
     "a linear $x$-dependence we study now a cubic polynomial and use the polynomial regression analysis tools of scikit-learn."
@@ -2147,7 +1471,9 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -2350,8 +1676,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "# Common imports\n",
@@ -2398,8 +1726,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "from pylab import plt, mpl\n",
@@ -2432,7 +1762,9 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "\"\"\"                                                                                                                         \n",
@@ -2459,8 +1791,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "# Read the experimental data with Pandas\n",
@@ -2501,81 +1835,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "            N    Z    A Element  Ebinding\n",
-      "A                                        \n",
-      "1   0       0    1    1       H  0.000000\n",
-      "2   1       1    1    2       H  1.112283\n",
-      "3   2       2    1    3       H  2.827265\n",
-      "4   6       2    2    4      He  7.073915\n",
-      "5   9       3    2    5      He  5.512132\n",
-      "6   14      3    3    6      Li  5.332331\n",
-      "7   19      4    3    7      Li  5.606439\n",
-      "8   24      4    4    8      Be  7.062435\n",
-      "9   29      5    4    9      Be  6.462668\n",
-      "10  34      6    4   10      Be  6.497630\n",
-      "11  40      6    5   11       B  6.927732\n",
-      "12  46      6    6   12       C  7.680144\n",
-      "13  52      7    6   13       C  7.469849\n",
-      "14  57      8    6   14       C  7.520319\n",
-      "15  64      8    7   15       N  7.699460\n",
-      "16  72      8    8   16       O  7.976206\n",
-      "17  78      9    8   17       O  7.750728\n",
-      "18  85     10    8   18       O  7.767097\n",
-      "19  93     10    9   19       F  7.779018\n",
-      "20  102    10   10   20      Ne  8.032240\n",
-      "21  110    11   10   21      Ne  7.971713\n",
-      "22  118    12   10   22      Ne  8.080465\n",
-      "23  128    12   11   23      Na  8.111493\n",
-      "24  137    12   12   24      Mg  8.260709\n",
-      "25  146    13   12   25      Mg  8.223502\n",
-      "26  154    14   12   26      Mg  8.333870\n",
-      "27  164    14   13   27      Al  8.331553\n",
-      "28  174    14   14   28      Si  8.447744\n",
-      "29  183    15   14   29      Si  8.448635\n",
-      "30  192    16   14   30      Si  8.520654\n",
-      "...       ...  ...  ...     ...       ...\n",
-      "238 3089  146   92  238       U  7.570125\n",
-      "239 3099  146   93  239      Np  7.560567\n",
-      "240 3109  146   94  240      Pu  7.556042\n",
-      "241 3118  147   94  241      Pu  7.546439\n",
-      "242 3127  148   94  242      Pu  7.541327\n",
-      "243 3136  149   94  243      Pu  7.531008\n",
-      "244 3144  150   94  244      Pu  7.524815\n",
-      "245 3154  149   96  245      Cm  7.515767\n",
-      "246 3162  150   96  246      Cm  7.511471\n",
-      "247 3170  151   96  247      Cm  7.501931\n",
-      "248 3177  152   96  248      Cm  7.496728\n",
-      "249 3186  152   97  249      Bk  7.486040\n",
-      "250 3194  152   98  250      Cf  7.479956\n",
-      "251 3201  153   98  251      Cf  7.470500\n",
-      "252 3209  154   98  252      Cf  7.465347\n",
-      "253 3216  155   98  253      Cf  7.454829\n",
-      "254 3224  156   98  254      Cf  7.449225\n",
-      "255 3232  156   99  255      Es  7.437821\n",
-      "256 3241  156  100  256      Fm  7.431780\n",
-      "257 3248  157  100  257      Fm  7.422194\n",
-      "258 3256  157  101  258      Md  7.409675\n",
-      "259 3264  157  102  259      No  7.399974\n",
-      "260 3275  154  106  260      Sg  7.342562\n",
-      "261 3280  157  104  261      Rf  7.371384\n",
-      "262 3289  156  106  262      Sg  7.341185\n",
-      "264 3304  156  108  264      Hs  7.298375\n",
-      "265 3310  157  108  265      Hs  7.296247\n",
-      "266 3317  158  108  266      Hs  7.298273\n",
-      "269 3338  159  110  269      Ds  7.250154\n",
-      "270 3344  160  110  270      Ds  7.253775\n",
-      "\n",
-      "[267 rows x 5 columns]\n"
-     ]
-    }
-   ],
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
    "source": [
     "A = Masses['A']\n",
     "Z = Masses['Z']\n",
@@ -2595,8 +1859,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "# Now we set up the design matrix X\n",
@@ -2617,8 +1883,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "clf = skl.LinearRegression().fit(X, Energies)\n",
@@ -2635,31 +1903,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean squared error: 0.04\n",
-      "Variance score: 0.95\n",
-      "Mean absolute error: 0.05\n",
-      "[ 0.00000000e+00  7.06492086e-03 -1.73091052e-01 -1.66020213e+01\n",
-      "  1.17385778e+00] 15.212327334149492\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x396 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
    "source": [
     "# The mean squared error                               \n",
     "print(\"Mean squared error: %.2f\" % mean_squared_error(Energies, fity))\n",
@@ -2694,92 +1942,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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OrITF9G+nc/z247E/Zubhsx8L6j0vbH6O7y7fgd1ib+fqRCSaKaxDzJx3APv779aPFwfJZ/j4omolAHfazySOlsPB4rCTsrEvZo+F7dvNVFXBqJIBAKzO2EL1vgwsZpg40UNa7chvbKwVh8PTqtrag8/nbyKbDZwOqKwykZRkUFpqwu2Gzp0NystNVFZCRoZBVZUJh6P+sdMB2V0MnE6orDSRlmYQE9PEcbyQX2CipNjE9uM2MXrHaKZ/O4NTNkw5bFtz7XC52WRgsUA1Lj4Z9Cnf+O5icszAdm6R1vMOGIh7wsRwlyEibUDnrEMsee6PiXm79b3jjVmQ8wvoXgZ7Hmx5exfJrOUBquh32Gsm3IznYmIobHUdx7rt/Iw9XNyq9+zu+jaz9r/e7OsJ7MbW5EB++zIsFoq//Q5fdpeQH7spHen/w0ig9gresdRWOmfdQVh25gLgmDUHIyUl6Pd9kLSZ09cm8rN3f8pHsQlggLv2pK239tKouulcBmDBjoUYXFRQQz4mE3Tv7sPtNpHYaT++ETNp6qxsXKyNGsfRnROPZF1xk+V4AcNoPDnO4YCiQv+laXv2mDl40ERxtpchB2fRc/8ZrOGMZvfpw0Vc788x4vKJjYWePX1YzP6eet3kNqvdhcncdt+b7e+/i2X/PiybN3WYsBaRH05hHWLmwgIAKu9eiNG5c5Pb+HwGNcU+EjItgef+u+hmbnpyDvGueOouW7Yc8vNQe63xZP5zBFXE0L27QY/B9Rc8VzbznrisJCqPkW+obckMZNU+zq796fF5uOB3szn7f2fRlf44HRYctd+APF7AMBHvNdOvOh7nzkmAf8Lbhm8P339hvJN3L9zG3j4VxMZB374+XC7/TPbk5NbXG5O0jpz39jFpZy7uSYcP6YtIZFFYh5LXi6nYP5XaSE9vdrNlJ+5lwI6DFA7LoM+Pk/jmWwunrB5OvCue9Z3MLDh4PHFxBjff7CI93cfgwT7S02HpUivp6QannuqlogL69LUTn2ii8TRtaStWs5XeM3rym+6/aX4jA85ddS4XfH4BMe4mTpoDVq+VzOoELnpqKB5zcPMFcjvl8sfz/sjezL1Nb9AJuBg+3bOKAfw0qH2KSMelc9YhZCooIHNoP3xpaRRt3tXotYe/eYB7vvgjp6w/hbv+c1eT73dYHQz8ZCQ7KpLIyjLo2rXt/+g6SltFCoe9lL9+9DecXmfQ7zEM/3XmFRVQcNBMYiyMXDqY8R8NwOZtbpzkcGU2F9+mF4LJIC7Wf5272QLxcQYOVz7fp2/BGPQxdy/44od8tDanv1uto/YK3rHUVjpn3QGYi/wTunyZWYe9tuXRHSz5eAmZFZkAPD35aeJccaRX1vfAbdNiGdN3IiPQ/Ts7ih4pPfjt+Ka/XLXKBWB4DQ79o3W7Yes2ExnpBvv2mfjoIyuJNi/dntlOr/0lTM7v2swOuzOSMZSsOYNnX9iCxWQjNc3AagFfkpWMWanYetiwplsZMtmGqeV74YhIGCmsQ6jufLUvI7PR81v27OLi984hpcY/4WxrZhoLX3gE8N8ZzKo/pahgspgOm4Bgt8HQ4QAmsrvDmBP8w+TG9X2o+jgTb7mHinITm7eY8XrhwAETmzebSbVUkVPyBr0OjiCt7tYvVQ12vKEg8PA/nXPZn16ACf/fNZMJKhIq+XjiJxR2LsBsBlvtlYKd4zvz2OlPkh6b0V7NICJNUAyEUF1YG4f0rN9bsJFJNZ3ZlpnH8S9M5awhVsxmf1fHrKVWpAkmq4nEKf6ZZylA98O2SOSrqb/nlzP6kOBMaPRK74O9Gb91PHGuOLoXdWdIfh+G5Pc5bA/TP59MZWzjqYilCaX8Y9az/ObeGwN/R0Wk/SmsQ8gUGAb396wPbvGw/tkqer3r77bsmFLF2cNtYatPji2nmwcyZNlq9jz+dzzDRzS9UZXBweXgrYCqKti714TPayJjl5chWz0kOxpPRU92JNPz6Z58vuRr8tKsVE8xkXoCJHUzSMvxb5Mam0av5N7t++FEoozCOoTMBbXD4LU96y9m5NK3vBxIoyyujIk39Q9jdXKs8fbuS+9vVpN+0IQzc1jTG2UCNzb9ks/hw1fjw+uBNWstlJbAxysWc8bHfUmvSie1wAcv4f8P+Lb3t2zosYHKuErOvP4Spo+f3A6fSiQ6KaxDyFxUxIMnwF9iH6fLXf/jr+X3UW2rYU2fb/lg1Ecs6fdCuEuUY4i3t39oO/b5JVi3bD6qfZ1a+3PKAAdXjXyMmrI4BmwbzqjvJ5DgSKRTWTajdo5i1M5RADj+52C1+R3MNjfpQ7fRZUAx1ek2EoxDl1YJLSMujprLr8LIOnySp0hHprAOIVPhQQodN/HUgjPYl74PgNeOX84TU59g7pArsJiDv2xHpCXewUMAsK/6DPuqz9pkn/FA/c1yNwL/BsBDAvmcSo05lTd6DmHEzvFALLig8stsNq7dj0H9pYY+SxUHuy6hPP2TZo+V5IKfrANbG1/8YHI4qPr9/LbdqUg7U1iH0NqKA5z03bXEemLpd7AfXuCC3/yaX425jbSY5m+SIvJDOGecRcUDjwRuxNPeUmr/izV9yWXFj5FWmcbYHWO54LMLsDsPv8Ss17Y/UZRYhM9Un8YOm4OPB3/Mhp4bcNgcbJmWzh3WU9qkPkvuduKe+xfWDevaZH8ioaSwDqGvyzsz1B1HUVwFVkc6B3IyOXdidstvFPkhbDYcl1wW8sPONnzkrX2UPRX+G/+8+rPXiKmIwWK2sXOnB6cTem/syowPhpFRefglYBd/ejF86n+cl3yQh6wDiY+NIS3NID4eLPEmsi7JYNDMOCytGIwy5+4g7rl/Yfn+u7b4mCIhpbAOIV/pGAC2jq/min9PCnM1Iu3DbDJz3cgbDnv+0LtM+ap9eEv94b1li5nSUhOV25y43yzGWuoizrWNbmWdyKbU/4b99fsyPszn/dgaDLOB1QrFWVWsn7Afa28HyckG1gQvjl7V9avbABgGaZNtXPXlAUwlxRhpGs2SyKGwDpG8XYX0238CAGN+PlrXqErUM8ebMcfbsQGjA5d5x8Nv4wH4en8h8+6fR+fSxgve9CzsyVlfn0U3R1zguezyeIZsbzxpbEOPDbw29rVG91v3dBrPlzO+4f++/w7PiSe1x8cSaRcK6xBY8a812O/eR3dXNnsytzDt1DHhLkmkwxvbdRw/v+56viveeNhrH1R/jL3ITk2NiYIDZrp93ZlBGzthuPx3csusiSVnTw45e3IOe2+1vZplH+XjjN2Ab5iVjOkWOh9vIi7d/wU61hJL18Ru7f75RFojpGH95JNPsm/fPtLS0ti1axd33303sbGxoSwhZKzr1mB95T/4PF7iXzyJ7LJsDqTtpH/mc8BF4S5PJCLM6j+HWcxp9fuqC7x89+cCqr9zUF0NpaUmqqtMdDWvo0dlF/oc7A04YbcT3gAfPtZlbye3Uy4ei4fqXsnMnHoVGZ0s9DghBkuMRsIkvEIW1gUFBSxevJhVq1ZhNpu59tpreeedd5g1a1aoSgipf1+zjM55E6nIeIPeZdkcTN7H7LKrMR//I8rDXZzIMS4+y8LYvx0+edP0/jbufeJydiX3A3M3crYMpd+BXnQpy2RA3gAG5A3wb/gtsGwTVcBqm5NtnfMoTS3nQI+DlPQqwBZr5qyzzuTUYW0zU12kJSEL67i4OGw2G5WVlSQnJ1NdXc2AAQNCdfh2U7zLQ/FOL/0n1a9V7PV6yT5wIulVGWRUXgHA/sGb8Z45n+ozj80vJyIRIWcof125C8OyF1+PnsAnkAKeRBvFVf2pcWewPd7Ct0kD6FTWlRh3DF1KuzBiby/YC2yo35XnYQ+r4l7CYi7FnbADb1IuJrMPI2kn5rgSLD4Tk/JjSfAEd4N/w2ym5tobcFym9cflcCFdz3rZsmWsWLGCrKwsDMPgzjvvJCEhocltPR4vVmvHv0nIU5lr6V5USu8PxzHwFP+Elw+Xf4wx2xvYpjy2gnFbJ5HdPbm53YhIKBgGjBwJ6458rXVeInxZe9ra5O6ExdkHkzsbi2MIZndnKmyxJFf0x2I0/W9UQVIBHouHgtRcJh3YTKrTQwI7SWILJjzYKMNEE//0du0Ke/eiNUvlUCEL6++//5558+bx6quvYrVaWbBgAWazmXnz5jW5fVsuJN5eC5N7XAbruq8hBh97LurLtL+lAfDkTY8y4fnx5KXu5kCyh/Lx8Vy36Mw2P357OJYWcQ8FtVfwOkxbuVyY9+45ql0YhsHTa1ewet0WEgpS6L6tF+nFaVjdVvrldyPWaz/i+ytjKtmduRevycv+jP3s6JKL2VfN0OJ8fjT7XsyDc8iZnEa1t/qo6owWHebvVhvIykpq8vmQDYPn5+eTmpqKtXZx5qysLA4cOBCqw7eLHR+7iMF/96Wq1VWAP6yrdvjPSlen7uSSL38VrvJEpCl2O76+/Y56N5f3+xWXn3P4885KH/nfeSgqLueFZY9gOViB1Wtl8L7BdC/qjtVrJdmRzJB9xwEwbO9QWNvg/V8DbGCVyUdBQgVes4/ClHL2Zxdg2HyUZ5RT0q0IS5yPof1GcdHpF5GYalZn/BgXsrA++eST+fDDD1mwYAFJSUls3bqVO+64I1SHbxd7P3JQdwVo4u76dX8Tiv3XiSZnaDFqkWgTk2im5zg7Pclk1LT5lDlLaTiAaQD713gpXOfF5zIo+8qFJ9dNbNVmqtlHamVnzIaZTmWdyK5MAaBbeRoj9vRq8ni7+ZYai4edmSVsOmknNVMOMGPiaUzsdnIoPq6ESMjC2mKxcNddd4XqcCFR9k1NIKy7OCp54tMnIdFJz6KeAPQfcfj9kEUkuqTEpB72XNp4YPwhT1Z2xjqiFx93deMcNowyYnAVJ4PPDKXZmMs6YfjM2Ko6YavJwG0GjymBzPIs4rxWBudnMXhpFiyF3KxcPrI8SE5MHl277SQmJvjVzpxTp+O48uqj+9DS5nRTlKOxs/58kgUT/1n+T7Zlb+O1otfwmjx0ndEzjMWJSERJTCT+pDOY9d/XYfP6oN6yJxleG2vC8CWRUnISKcWTiKscRp+CPkAfDgC79rpY2/srtnXZRKbxNX/8bBOWI8xUsn3yEY6fXArH6D0wIlVIZ4O3RiRMMHu/51dkO8ys776JYXuPo8LuJMnlv4TLYt3O4HWnYmRmtvlx29OxNFEjFNRewVNbtcxUWYH1qy/B8JGaEk9pWesnmDmrDe7+6B0qcl2M2T6GIXuHYKb+lNye5EJiTUlYu3jIOq2K7BFObP4zdyTOv5P0LTspffM9PGPHtdXHanfH0t+tsE8wO9aU53vp5DDhMXvYN87MsL2Q5PJPN7PgogcrMTLOD3eZIhJBjMQk3FN+5P8lKwn3DwggM/D7mVMpd5bh9nnI3+hh67NOytbvZeBWFz3KazsQZTF4NiWwF9jWeRv/y/kf5YOGktI/kYVff4Y9gsI6Giisg2S/ez7mZ5eQ4HMDUGDph5m/sCtjN9d/fgeVlp8Qa86ji/0DYqoKcA8ZSammZ4pImCTH+CenZYyFIWMBevLvr59l6dKXMRwwZvMYxm0dS6fyTPrn96d/fv/Aez/7+ADlD67GlWXCPcEgbohB+lgf8bVrpQxKP44ku+4bEUoK6yC98EoK3SqfZLRlLl2ry1jXoxfZgDN+G913FQEPgxfwZzmu088IY7UiIoe7aOwlXDT2kkbPOSp8rHmsgqJ3KnAX7SKztJqsii5kAZQYsAXAhNvsY32v9VTEVvBm6hsMP+0nDB/ei645VmIzOv4NrCKdwjpIg/b4L4NYfNrdXL/oTPKmfkA2kD+mL4Vv5zbe2GLBSDl8BqiISEcTm2Rm/LwUmJcC7s5sOrkHTw7qRVG3oWTv6sXA7X3JLEulV3E2o3NH17/x8wpq2MAWDLZmVuCM91Cd7qLo+BJsnd3E9HZCD/8s9JGdRulSsqOksA6Gp349XA5CtSWVQfu7ADDy0uEY6RlhKkxEpA3ZbIzPGMbJ//0Cx6zj8PWqhF7+W7NWV8SQty2LMofBm7Yy+u0fgs1jo2tJV44rrB0S3w2sqV9AZWfmTsriy1iT+CUF9g8Y600lMa2GLn2LMFuPbm6zr3cfan52bdTcmlURll3oAAAgAElEQVRhHYSyXfmBx9kH41j5r1x6emLJzdrFmRNav3yfiEhH5R5/IravviD2tVcbPR8P1F3bMjAeHh8LZbHwnTuRtNKhmH02kiv6k1E8EovPTkJVT3oX9m60j7La/7Z+UUZ1TClVsaXs6PolVXEl1NjLOZixgeu+KWPKziBrHT02omatHw2FdRDyNu2H2ksf+h/syWdvrKMnvdjcu4CZUfKtTkSiQ/Uvb8bbuw8mR02z28QBzd9I2Qk4cTu/48CmBKorzKyo3IqvJB6bx0a//H50K+lGvDOFzPJe9Do4otG7K2IreDnBiTetEnuqk9hUN/HHFZLS1YHN5u9I21Z+QLcPv8S26nOFtdQrzD1IKv6hHavPyrh1tcM84+LDWJWISNszklNwzL28TfZVO3mcXxgG6wrWUOIsAQP2biuneL8F50Yz8WusuCtMxBSa6FNmkORIIokkqMr0L0sK8Im/R14eW87qfqspTBqLc/xAZv7va4ad6yUjxdcm9XZkCusgVB4oCYQ1QKwnhi1dtnDKZUPCWJWISGQwmUyM6DSq/olmbu7o9nj442t/ZseqTQzcPgiry06XwmyG7xpMnCeGZEcyUzZOafSePSPWsMPkY1unQg5mlVORXUnRyHxMParo0b0zV024mlhr5N+NTWEdhKqiyka/70opI+8v1czuNbqZd4iISGvZrFb+dM4foInVzLxe2PGJk72vVVGW78GzZTGZRWOJd8YT54pjcH4nBud3gg3Ae7XvMXl5ucdr2FKGkZhlJm6cjW5nWkjsasaEieyELljMkXHZmcI6CK5S/7mbDb1X0euKy5l4diwzup4a5qpERKKHxQIDJsUwYJL/ls4JV27mrW0L2fTjs3D1nIjn8zjYG0fCzkS6707F5jaT5LEwenc/oPa2rf8D7wL4ttMO1vdcz87J+1h86Z8wm37YColGQgJGYtO3B21rCusgeKv8l2557G7GX5sQ5mpERMR3wgTOWbEc/rACWNHkNhvTk/l7zgScpkSSq9MYuD+HvnmD6XuwL30P9qV8QzmvvHUuUzccTwK5ZPEhrZkybMTEUPrGu3iGj2yTz3QkCusgGDX+6wF99mN/EoOISCRwnjWb2OefxVRYgMVswus7/Lrt44CHN61p8MwKvGk2Sp19WW3MIrlyDMkbFrGr9tW1vb6mU8JSzi/IDerybSMjE19qWlt8nBYprINgcvn/1IzIn6MgInJM8HXpSsnKzwD/SlXFrVj0xAQMKvCy8qSV9C1JZW3PtfQu6M2IXWOBsfwzowhOHkZK1zh8mDAlWhh9WTzJWT9suLwtKKyDYHHZADDHq7lERI4FSVkWpnw2hdyPXFx49mgKt3t5Zd575Hxro19RBizb32j7LQ+4WNNnC+tHbmT3CZvwJDlJj83g/kl/I6tuhZN2pPQJgtXjn9BgTYkJcyUiItJWEjPMDJvjHzLtNMDKNa9OY03uBh6/9x/0394Vq9eK2TDTqawTx+0/jglbc5iwNQfXKy6qYqpwW918/pdtzJqjsO4QbG7/H2ZMqm6CIiJyLBvZJ4f7H1vIuoI1eH3ewPN7NznJf9ZKymcW+hbbSau2U2kykR07NCR1KayDEOPyzwBP7BKaiQQiIhI+MZYYjs8+ofGTXYHaK3aLd3lwVkL/XmZiEkNzHlthHYRYt79HndpTq2uJiES79F6hj87wTW2LIPFO/0XvWf2zW9hSRESk7UVtWH/8+Du8edeLhz3v9XhxVjkDv7trnMQ5/T3rLsd1C1l9IiIidaI2rN1/ddBlcU/ytx5o9PzSCY/z6ci3KdlfDMCBjXswY6YqppKYeHs4ShURkSgXtWGd4EjB7rVTsDWv0fPdCgbSuawb361YDcDBLf7Xq+2Vh+1DREQkFKI2rM2G/6NXl1Y1et7m9feeS3YeBKB0byEAjpjG24mIiIRK9Ia1z//RHWWNQ9ju9t/4pOpAGQCVef6fTlt1CKsTERGpF71hXduzdlXUBJ5zVjmx+fy3FnWXOvzPlfhD2mWrQUREJByiPqwdla7Ac+V5pYHHRqX/zjWuYn9Iu+1OREREwiF6w7p2GNxT7Qg8V55fH9ZmR23TlPuXxfQmuENXnIiISANRGdZejxczdWFd37OuLq6f8W1z+c9d26r89wW3pNlCWKGIiEi9qAxrV4OA9jo8gcdVRfXrodpdcQDEO/x3L0vonhqi6kRERBqLzrCuqT//7HXWr6riLK+f8R1be4vRpGr//cCzc7qHqDoREZHGojKsPTX1PWufq75n7Sivn/Gd4EjB7fKQVpkJQP9TQrMMmoiIyKGiMqxdjvrJYobbV/98Rf1ks0RHMls/2YLNZ6M8royM/l1CWqOIiEidqAxrj7O+Z90wrN3V9cPjdq+dLa9/BUBZfEHoihMRETlEVIa1u6bBZVieBg8bTDwDqPjeH9JVcaWIiIiES1SGtafBpLKGYe1zehptZy/yzwh3xWkRDxERCZ/oDGtXfc/a5DEFHje8jAsgs9S/frUvofHzIiIioRSVYe11NghrX31YG25vo+2yS3sAYE3XDVFERCR8ojKsPQ2Gu83e+iYw3EaT2yf3SGv3mkRERJoTlWHtdTUMa0v9C7Udbo+5/nWP2UP/8f1CVZqIiMhhojKsG56zNvsahHVtRuenHAg8lTfkaQacNT5UpYmIiBzGGu4CwsHrbrpnbfL6z1/n9drO1tOsjD1vKGdMeTzk9YmIiDQUnWHtqp9IZvHVTx6rC2tzLPzi0Zkhr0tERKQpUTkMbngbhLW3/vtKXS/bZLcc9h4REZFwicqw9ja4RMvaoGddNzPcZIvKZhERkQ4qKlPJ527Ys24Q1j5/L9sSq+uqRUSk44jKsPZ6GvSsG4R1XXBb4hTWIiLScURlWDfsWdsahnVtz9oWbw95TSIiIs2JyrD2euqXxbR67Q0e+4Pbnhgb8ppERESaE5VhbXga9qzrw9oSCOu4kNckIiLSnKgMa1+DsLZ76sO6LrhjUxXWIiLScURnWHsbXrplpabaf/vRuiHxuJSEsNQlIiLSlJDewWzHjh288cYbxMTE8NVXX3HDDTcwfPjwUJYAgNHgnDVARUkNcfG2QM86PjMl5DWJiIg0J2Rh7fV6WbBgAY8//jhms5nZs2djtYbnbqc+b+OlMMuLKunULRlb7ZB4UlZiOMoSERFpUsjScv369RiGwZIlS3A4HKSmpnLBBReE6vCNHNqzri6qAMDuiQEgubPWrxYRkY4jZGG9f/9+1qxZwwMPPEBSUhK33HILNpuNc845p8nt09LisVrb7h7dWVlJgccWs6nRaxaXh8QYK1afFa/JS6+BnbG04bEjTcO2kpapvYKntmodtVfwjvW2CllYJyQk0LdvX5KS/A06ZswYvvzyy2bDuqSkus2OnZWVREFBReB3t9Pd6PWCfcVs37DH/5rVRXEbHjvSHNpWcmRqr+CprVpH7RW8Y6mtmvvSEbLZ4CNGjKC0tBRv7Uzs/fv307t371AdvhHD23gY3FFeTUV+GQAuqyscJYmIiDQrZD3r1NRUbrnlFu655x7S0tIoLi7mF7/4RagO34hxyAQzV4WTqqIKrJhxWxTWIiLSsYR0Ovbpp5/O6aefHspDNsnwNQ5rd5WDmuJKkkjGo7AWEZEOJipvinJoWHtqXNSU1QCoZy0iIh1OVIY1h4S1t8aFs8I/qcyrsBYRkQ5GYQ34HG7clQ4APBZ3U+8QEREJm6gM60OHwX0uL+4qf1h7zZ5wlCQiItKsqAxrvI1/NVxePDX+kPaqZy0iIh1MdIa10bhnbbgNfLU3SvFZ1LMWEZGOJTwraYTZocPguA18Dn9322f2NfEOERGR8InOnvUheWzyAIVOANzx6lmLiEjHEp1hfUjHGi/Elfvvx2r0ywh9PSIiIkcQ1WFdY/PfCMXm8pJW3hOArmeMCVdVIiIiTYrOsK4dBi9M2QVAp4I+ZJV3xWVxMWHawDAWJiIicrioDmtn4npcFhddSv296v1p+0lItIexMBERkcNFZ1jXnbM2u9mXtT7wdF5GVXjqEREROYKoDGtTbVhXJmRR0flg4PmqnglhqkhERKR5URnWdT1rn9VKyuWzAk+n5mSFqSAREZHmRWlYm/w/zXD6BUPJTyrEZXFx/IxB4a1LRESkCVF5B7NAWJvAZrNgW9SH3XmVnD2ic3jrEhERaUJUhrUpMMHM/+Pk6bpcS0REOq4oHQb3/zCZTeGtQ0REJAhRGdamwDC4wlpERDq+qAzrhhPMREREOrqojKu6nrXJEpUfX0REIkxUppWpwWxwERGRji4qw5pAz1ppLSIiHV9UhnVgGNwclR9fREQiTFSmVWAYXD1rERGJAC3eFGXZsmUt7iQtLY1Jkya1SUGhYDL831F0nbWIiESCFsP68ccfZ9SoUUfcprS0NMLCWrPBRUQkcrQY1ieccALz588/4jb33HNPmxUUCjpnLSIikaTFtNq5cycul+uI2/zyl79ss4JCwVT7sTUbXEREIkGLPWu3282dd95JYmIic+bMYejQoYdtk5CQ0C7FtZe6nrXZqp61iIh0fC2G9T333EPv3r0pLCzk1Vdf5R//+Adjx45l1qxZJCcnh6LGNheYYKZz1iIiEgFaDOvevXsDkJmZyc9+9jMAVq9ezTXXXEOXLl3461//2q4Ftoe6sDYrrEVEJAK0aj3rsrIyXnvtNV5++WV27dpFt27d2quudhWYYGa1hLkSERGRlrUY1o899hgjR47k5Zdf5v3336dfv378+Mc/ZtasWSQlJYWixjZXf85aYS0iIh1fi2H9yCOPkJSUxMyZM3nhhRcYPHhwKOpqV4FhcE0wExGRCNBiWE+ZMoUHH3wQu90einpCoi6sLepZi4hIBGixa7lgwYJjKqihQc/aprAWEZGOr8WwrjsvXVRUxK233sovf/lLqqurufPOOykrK2v3AtuDGYW1iIhEjqBP2i5cuJCxY8dit9uJj4/nwgsv5C9/+Ut71tZuTL66sG7VZHgREZGwCDqsO3fuzPnnn098fDwAQ4YMidjZ4GadsxYRkQgSdFiXlpYCYDL5L3uqrq5mz5497VNVO6s7Z221q2ctIiIdX9BpNWHCBGbOnInL5eLqq69mw4YN/O53v2vP2tpNoGetc9YiIhIBgg7r6dOnM3DgQFatWgXAbbfdRt++fdutsPYUmA2unrWIiESAFtPqueee4yc/+QkA/fr1o1+/foHXXn75Zc4///z2q66dmHXploiIRJAWw/qf//wn33zzTZOvbdy4MSLDuv6ctS3MlYiIiLSsxbBOS0tj3bp1zJw5E4ulcU909+7d7VZYezJrgpmIiESQFtPqxRdf5NNPP+W1115j3LhxzJ49OxDaOTk57V5gewhMMItRz1pERDq+oC7dmjhxIvfddx9du3blt7/9LS+//DIej4fJkye3c3ntw1x7UxRbjHrWIiLS8bVq2akJEybw61//mmXLlnHllVe2V03tzmT4RwasCmsREYkAQYf1wYMH+fOf/8zUqVPJzs7mzjvvbM+62lXdMLgtTsPgIiLS8bXYtczPz2fx4sW88sornH766SxdujRwffXOnTvp3bt3e9fY5syG/y5s1phjazUxERE5NrUY1qeddhpdunTh9ttvp3fv3hQVFVFUVIRhGCxZsoRHHnkkFHW2KbPPPwxuj1XPWkREOr4Ww3rUqFHMmTMHgH379jV6rbKyslUHczgcnH/++Zx00knceuutrXpvWwpcuhUbE7YaREREgtViWF977bVMmDChydf69OnTqoM99NBDDBkypFXvaQ91s8Ht8RoGFxGRjq/FCWbNBTXAyJEjgz7QsmXLGD16NN27dw/6Pe3B6/Firv3Ymg0uIiKRoMWw/vTTT1vcSd3iHs3Ztm0bO3bsYOrUqcFX1k5c1S4AvCav1rMWEZGI0GLX8t1332XixIlH3OaDDz5g/PjxR9yH3W5n8eLFrF69GrfbzTPPPMPll1/e7HvS0uKxtmGYZmUlAVDq8QLgM/kCz0ljapfWUXsFT23VOmqv4B3rbdViWL/wwgu8+OKLABiGgclkCrxW9/uIESOOuI9rr7028NjpdFJdXX3EoAYoKaluqbSgZWUlUVBQAUDR3iIAfGZf4Dmp17CtpGVqr+CprVpH7RW8Y6mtmvvSEdSlW4sWLQL8QRsT88NnUL/99tt89dVXuN1uXn/9dWbOnPmD9/VDuRxuwN+zFhERiQQthnXDy7UuuugiXnnllcDvLQ1lH+qMM87gjDPOaF2Fbczj9J+z9pm9Ya1DREQkWC1OMDMMI/DY52vcG12+fHnbV9TO3DXqWYuISGRpMawbnqNu+BgaB3mk8DjrJ5iJiIhEghaHwXft2sUdd9zBsGHDqKmpwe12Y7P5b9N5aHhHAo/L37M2FNYiIhIhWgzrxYsX891337F27VpsNhtjxoyha9euDBw4kPz8/FDU2Ka8Tjdm1LMWEZHI0WJYjx07lrFjxwZ+d7lcbNq0iY0bN5KSktKuxbUHj9ODHat61iIiEjFafb9Nu93O8OHDGT58eHvU0+68Lg9gVc9aREQiRosTzI41gXPWZoW1iIhEhqgLa59Xs8FFRCSyRF1Ye13+sNY5axERiRRRF9Y+t8f/U2EtIiIRIurC2utWz1pERCJL1IW1z61z1iIiElmiLqy9HvWsRUQkskRdWPs0DC4iIhEm6sLa6/GHtM8UeYuQiIhIdIq6sDY0DC4iIhEm6sLap7AWEZEIE31hXXsHMwOFtYiIRIaoC2uj9py1oXPWIiISIaIurH1ef0hrGFxERCJF1IW1etYiIhJpoi+svXVhrZ61iIhEhugLa5961iIiElmiL6zretYorEVEJDJEX1jXZbR61iIiEiGiL6w1wUxERCJM9IW1r+7SLYW1iIhEhqgLa2rDWsPgIiISKaI2rHXploiIRIqoC2sNg4uISKSJurDGW/vTFNYqREREghZ9YW2oZy0iIpEl6sLa8NSGtVlhLSIikSHqwjowDG5RWIuISGSI2rA2LOEtQ0REJFhRF9YmT+3MMmt46xAREQlW9IW1T2EtIiKRJfrC2usPa5M16j66iIhEqKhLLLPPf7LaZIu6jy4iIhEq6hLL7PN/ZLNdM8xERCQyRF1Ym2p71ma7TlqLiEhkiLqwtvj8IW2JUViLiEhkiLqwNnv9IW2Ns4W5EhERkeBEXVjX9axt8fYwVyIiIhKcqA1ra3xMmCsREREJTtSFtdXrH/62J8SFuRIREZHgRF1YW3z+sI5JVs9aREQiQ9SFdV3POjYpPsyViIiIBCd6wzo1IcyViIiIBCcKw9o/CzwxIzHMlYiIiAQn6sLaVtuzTkhXWIuISGSIqrD2erzYPP6wTsxKCXM1IiIiwYmqsK4uqcaMGa/JS0yCZoOLiEhkiKqwriwqB8BldYW5EhERkeCFbDWL3bt389BDDzFkyBDy8vJITU3l+uuvD9XhAagurgTAY1FYi4hI5AhZWJeWljJjxgxOO+00AGbMmMHkyZPJyckJVQnUlFZjAtwWd8iOKSIicrRCFtbDhw9v9LvP5yMuLrS3/KwuqSSBeDwKaxERiSBhWdT53Xff5aSTTqJfv37NbpOWFo/VammzY2ZlJWHx+gDwWtxkZSW12b6PNWqb1lF7BU9t1Tpqr+Ad620V8rBetWoVX3zxBXfccccRtyspqW6zY2ZlJVFQUEHpwXKyScRjdlNQUNFm+z+W1LWVBEftFTy1VeuovYJ3LLVVc186QjobfOXKlXzyySf89re/paCggG+//TaUh8dVWQOA1+wJ6XFFRESORsjCesOGDfzqV79i7dq1XHrppVx33XXk5uaG6vAAeGr8s8C9OmctIiIRJGTD4Dk5OSHvSR/KXRfW6lmLiEgEiaqbongd/pD2mb1hrkRERCR4URbW/uFv9axFRCSSRFVY+9z+HrWhnrWIiESQqAprw+2/ztpn8YW5EhERkeBFVVj7XHU9a4W1iIhEjqgKazwGAIZ61iIiEkGiK6xrT1X7zEZ46xAREWmF6ArrukngbXfLcRERkXYXVWFtqpsEHpblS0RERH6YqIotk7f2u0lUfWoRkdD67rsN/P3vD+PxeDj++BNwOp0AzJ17BUlJza+O9dJLz3PBBReHqsyIEl09a5/J/9MaVR9bRCSkhgzJYdSoMeTkDOfKK3/OddfdyKhRY/jlL6/B42n+plQvvfTvEFYZWaKqj2n2+k9Wm2wKaxGJDhdfHMd777XtP/Wnnebh+edrWvWeCRMm8vTT/+CLLz5n2bL/MGLEKHbv3sXpp0/j+ONP4P3336WysoL/+78n6NWrNyeeeDJ33XX7YdtFq6gKa5PPH9Jmu2aYiYiEWnZ2F/bv38cFF1zM8cefQHl5GTfffAPHH38CP/rR6Tz22MNceeXPAXA4HE1uF62iKqzNPn9Im2MU1iISHVrbA25PeXkHmDbtTL79djUbN67HYrFSWlrS5LaGYQS1XbSIqvHgQFjbo+o7iohI2H355SpcLhd79+6hsLCAyy+/igsv/EmjbcxmM4ZhsHXrFlasWNbsdtEoqlLL4vV/XGusLcyViIgcuzZt+o61a7/F7XbzzDNP4nQ68Xg8PPzwY5SUlLBy5fs8+ujfSE5OprKykpUr32fy5B9x4oknsWjRQwDMmjWn2e2ikckwjA55O6+Cgoo221dWVhIFBRWsGPYv+uYPZceVmznrXl0e0JS6tpLgqL2Cp7ZqHbVX8I6ltsrKavrStqgaBrf4/D1rW7w9zJWIiIgEL7rC2usPaXtCTJgrERERCV5UhbW1tmcdkxwX5kpERESCF1VhbfH6J5bFJMWHuRIREZHgRVVYW33+sI5LUViLiEjkiKqwtnn856zjUhXWIiISOaIqrO0e/8SytG7pYa5EREQkeFET1jXlNdi9dnz4SO6SFu5yRESkjWzZsoUbb7yGN99c8YP38dJLzwcev/POf5k2bcoP2s+BA/u5+OJzuf76q7n++qt55JEHf3BNDUXNHcyKdx8EwGGvwWLVvcFFJDokX3weMe+906b7dJ42lfLn/9Om+zwaAwcOZMSIUUe1j5de+ndgLe2pU6ezePHff/C+LrnkcmbMOOuo6jlU1IR16V7/TeCdNkeYKxEROba98cZrPPHEo1x44U/Yvn0rpaVlzJhxFl9++Tl79+5h4cIHeeedt8jN3U56egZ5eQe45Zbbyc3dzl//eh9xcXH89rd/YN68XzFr1mxmzz6vyeMsWfIMO3fuYPjwoezcmUuXLl0B2LFjO88990/69evPrl07ufTSn2KxWPnb3/6C2+0mJ2c4e/bsonfvfsydeznLl78SWJ5z6NBhjB9/IgAvvPAsmzdvoqqqknvv/SsWi4Vly5by0UcrD6tl6NCcwIphn376EaWlJVRWVnL66dPo06fvUbdp1IR1xYESkkjGZVVYi0j0CEcP+MwzZ/Hf/77OwIHHcfHFl3L77b+mpqaK22+/k4ceup+vvvqCrKxOnH32OZjNZh566C98+eUqTjzxJO69935+8YufsXLl+1x44SVMnTqtyWPs2LGNt99+g2effZmsrCTWrr0p8Np99/2Z66+/iWHDRvDNN1+zaNFD3Hvv/Zx88mS+/vpLLr/8KgAuueR8Jk48ibPPPoclS54OhG2diRNP4cILL2HevJvYunULxx03mNmzz2X27HOb/eypqWlceeU19O3bj+LiIn7+8yt46qnnSEpq+jaiwYqasK4qqiSJZNxWZ7hLERGJCt26dQcgMTGJrl39j5OSkqiurqZTp878/e8Pk5KSSm5uLgMHHgdAWlo6l1/+Mx577GFeeml5s/vOzc0N7B+ga9dugcfbt2/lyy9XsWbNt7hcTuLj45rcrmvX7uTm5tK3b/8mj9G9ew8AUlJSqa6uAuDVV//DypX/O2zboUNzuPrq64iLi6Nv334ApKdnkJaWzrZtWxg1aswRWqplURPWNaX+hnYprEVEwu53v7uVZ575N9nZ2YEgBHC5XHz33XrOOms2ixY9yK9+Na/J9/fu3Ye9e/cEft+/f18giPv3H8ikSafSv/8AXC4XH330QaPt6uzbt4c+ffoAYDL551tv3bqFAQMG1j5nOuy4c+acx5w5TQ/LA4ERhX79+uPxeCgoOBgYnj8aURPWrvJqADwWhbWISHv66qtV5Ofn8eabKzjppEls376Vt99+k8zMLNau/ZYdO7Zx2mlTeeCB+xg+fAQbNqxj9+6d9OrVh2effZqRI0czevRYfvGLq7FYLNxww82HBWe/fv2ZOnU6d911B0OGDKK8vIxPP/2IMWOO57bbfs8LLzxLt249KCoqZMqU0wLvc7vdLFnyNNu2bWXq1OmBXvWgQcfx+OOLiIuLY8+e3VRWVvL668sZMGBQoP7hw0ditR45Njt16sw///l/DBgwkL1793DVVdeQnd3lqNs0apbIXDRzITlvjOP7Hl9x3upr2mzfx5pjaam5UFB7BU9t1Tpqr+AF21ZvvrmCAwf2H3ZuuiOJ+iUyvTUuADxWd5grERGRUMvPz+PTTz8K9OwjTdQMg/scXv9PiyfMlYiISGt88MF71NTUNHpu8uQfER8f/K2jO3fO5u67/9LWpYVM1IS14fQB4LP6wlyJiIi0RsNzztEqaobB8Y+CY9g65Cl6ERGRZkVNWJs8tTMJ7eGtQ0REpLWiJqzNHv9HNdmi5iOLiMgxImqSy+z1L95hjtUiHiIix5KOtOrWm2+u4Kc/vSSw6tb55599VHXViZqwtnhsAJjjbWGuRERE2lJbrbpVZ+rU6SQmJv6g/fTs2Yt77rmfRYsWs2jRYvr3H8DkyT86qtogimaDW73+kLYnxoS5EhGR0Ln49fN4b3fbLpF5Ws+pPD+z+QVConnVrZyc4YHntm7dTPfuPVp1iVlzoiesPf6ZZbakuBa2FBGRoxHNq241tHTpS4FjHa3oCWuvv0cdl1c/8osAAAt6SURBVHr033BERCLFkXrA7S0aV92qU1xchMvlapP7gkMUhbXdUxvWGT/sPISIiLSdY3XVrTqvvvofzj47uB54MKInrN3+b1bJ2WlhrkRE5NgWzatugf8Lx+bN37fpgiFRs+rW67Gvk+hMJOmtNHqO7ttm+z7WaKWf1lF7BU9t1Tpqr+Bp1a1jSKw7FoC0HllhrkREREJNq25FgPKCcqw+Kx6zh6RmvrWIiEjHpFW3oiSs87fmAeCw1bSwpYiIdDRadStKhsGL9xYB4LQ5wlyJiIhI60VFWJfnlQLgtiqsRUQk8kRFWFcWlAPgsjrDXImIiEjrRUVY1xRXAuBWWIuISASKjrAurQbAY3GFuRIREWlrHWmJTJfLxV/+cg///Of/8cAD9/Hee2//4JoaCuls8M8++4x33nmHjIwMTCYT119/fUiO6670n6v2WtwhOZ6IiIROWy2RecEFFwP+JTIXL/77D9rPsmX/wWazc9llV+LxePjxj2czcuQYMjMzj6q+kIV1TU0Nd911F2+88QZ2u50bbriBzz//nAkTJrT7sT3V/pD2WhXWIhJddl28lcr3ytt0n4mnJdPr+QHNvh7NS2Tu2bMncI9yq9VKWlo669evOerLz0IW1mvWrKFr167Y7f6lKkePHs3KlStDEtbeGo//p8XT7scSEYl20bxE5vDhI/jwQ//CIRUVFezZs4uqqqpmtw9WyMK6qKiIhISEwO+JiYkUFRU1u31aWjxWq6VNjh3fxX/XMlOWqdn7rko9tVHrqL2Cp7ZqnbZor6x3R7dBJa1nt1sZNmwQWVlJZGamM2TIQLKyksjOzsRi8ZGdnf7/7d1xTNT1H8fxJ5xiCfwErpspwQRlpPZDMF1uV8pKNqpf/TR/6xe6+qOyWiOb2R+/cm1tzDZW/uaiLWO6X/5BKx1Z7teaGr8g06FsQkt/ICEgLikvCO4OhAPu8/sDu3nGle7X7vv17vX4774cuzev+3558T2+dx/ef38n6enpfP99DxMThbhcqbhcqWza9AJvvvkmX3zxRegE72pNTb3k5MwLZbRgQQ6pqTfhcqXS2dnB6dPNdHT8l5GREdLT/4TLlUpq6k0sWJAT+p6cnHn09fVy111FOByJYXk7HIkUFS0iISGB2bNdJCUZXK5UPvjgAw4dOvSreZYsWcLmzZspK/sbiYkT1NbWkJyczJIlS8jPz/2/n8uolbXT6Qz768Lv9+N0OiPe/+efh/+wx964bzP/+Vc9fy/dpA/G/x1aPOD6KK9rp6yuz42eVyAwTn//EDNm+BgZGWNgYBiPx8fQ0Cg+3wjbtr0QWiLz4sV+fL4RPB4fgUCAxsYmHnzwr7z+ekXEJTIzMubQ2dmFx+PD5Uqlo6OLtDQXHo+P+fPzWLbMHbZEpsfjw+cboaOjK5RrZ2cXTuccPB4fweDkAlK/LJE5MRHkp58m30l05fwlJQ9RUvLQlDN5PD683kEWL76TW2+9lfHxcQ4c+Dc5OQuv+bmMVOpRK+vCwkIuXLhAIBAgKSmJkydPsn79+mg9PH/+y51ReywRkXgWz0tkejweqqr+SWHhUoaHh9my5R/XtKzm74nqEplHjx7l4MGDpKenM3369N+8GvyPXiLzRv4LNZqU1fVRXtdOWV0f5XXt4mGJzKi+dcvtduN2u6P5kCIiIqElMn0+H52dHaEz6htFXKy6JSIiNy4tkamyFhERm9MSmXHycaMiIiI3MpW1iIiIzamsRUREbE5lLSIiYnMqaxEREZtTWYuIiNicylpERMTmovpxoyIiInL9dGYtIiJicyprERERm1NZi4iI2JzKWkRExOZU1iIiIjanshYREbG5mF4i89ixYxw6dAin00lCQgLl5eVWj2Q7jz76KDNmzAAgMTGRPXv2MDAwwPbt28nKyqK7u5uXXnqJW265xeJJreHxeNixYwdtbW3U1tYCMDo6SmVlJbNnz6a7u5tnnnmGnJwcAD799FNaW1tJTEwkOzubxx57zMrxo2qqrD7++GM+/PDD0D62bt061qxZA8R3Vj09PezYsYNFixbxww8/kJaWRnl5+W8ee7t27cLv9+P1enG73dx3330W/xTRESmrqqoqTpw4Ebrfc889h9vtBmI0KxOjhoeHzerVq83o6Kgxxpjy8nJz7Ngxi6eyn7fffvtX21577TXz2WefGWOMqaurMy+//HK0x7KNzz//3NTV1Zm1a9eGtr333numurraGGNMW1ubKSsrM8YY09vbax5++GETDAaNMcY88sgjpqurK+ozW2WqrGpra8358+d/dd94z+qbb74xhw8fDt2+//77zbfffhvx2GtpaTFPP/20McaYsbExU1JSYrxeb/QHt0CkrKb63WVM7GYVsy+Dt7S0MHfuXJKSkgBYunQp9fX11g5lQ+3t7VRXV1NVVRXKp6GhgaKiImAyt4aGBgsntFZpaSnJyclh2+rr60P55Ofn09bWht/v58iRIyxevJiEhAQAioqK+Oqrr6I+s1WmygqgpqaG3bt388477zAwMAAQ91kVFBSwevXq0O1gMMjNN98c8dj78ssvKSwsBGDatGnk5uaGnVXGskhZAbz77rvs3r2b6upqLl26BMRuVjH7MnhfX1/YL46UlBT6+vosnMieNm7cSEFBARMTE2zYsIHk5OSw7FJSUhgcHGR8fJxp02J2d7kukfat/v7+sO2/ZBnPli9fTnFxMRkZGTQ0NPDiiy+yZ88eZXWFw4cPc/fddzN//vyIx15/fz+5ubmh70lJSaG/v9+qkS1zZValpaVkZmYyc+ZMampqqKio4I033ojZrGL2zNrpdDI0NBS67ff7cTqdFk5kTwUFBQA4HA6WLVvG8ePHw7Lz+/3MmjVLRX2FSPtWRkZG2PahoaG43+eysrLIyMgAYMWKFTQ1NTExMaGsLmtsbOT48eO8+uqrABGPvavz8vv9oVzjxdVZ5eXlMXPmTGBy32psbASI2axitqwLCwu5cOECgUAAgJMnT1JcXGztUDZz9uxZ9u3bF7p97tw5srKyWLVqFc3NzcBkbqtWrbJqRFsqLi4O5XPmzBluv/12UlJSuOeeezh9+jTm8sftNzc3s3LlSitHtdz27dsZHx8HoLu7m8zMTBwOh7Ji8t8pX3/9NVu3bsXj8dDc3Bzx2CsuLqalpQWAsbExOjs7Wb58uWWzR9tUWVVWVoa+fu7cObKzs4HYzSqmF/I4evQoBw8eJD09nenTp+tq8Kv8+OOPVFRUsHDhQvx+P+Pj47zyyit4vV7eeust5s6dy/nz59myZUvcXg1+4sQJPvnkE44cOUJZWRlPPvkkAJWVlbhcLnp6enj22WfDrgY/deoUDoeDefPmxdUVzlNl9dFHH/Hdd99x22230d7ezhNPPBH6f2I8Z3Xq1Ckef/xx7rjjDgCGh4fZsGED9957b8Rjb9euXXi9XgYHB1m5cmVsXOF8DSJl1dXVxaVLl3A6nbS3t7Np06bQcRiLWcV0WYuIiMSCmH0ZXEREJFaorEVERGxOZS0iImJzKmsRERGbU1mLiIjYnMpaRETE5lTWIiIiNqeyFpEwTU1N5Ofns3//fqtHEZHLVNYiEhIMBtm2bRsrVqygtbXV6nFE5DKVtYiE7N27l+zsbNavX09bW5vV44jIZVpKSUQA8Hq97Ny5k5qaGsbGxjhz5ozVI4nIZSprEQGgqqqKNWvWkJmZSTAYJBAI0Nvby5w5c6weTSTuqaxFhLNnz7J3717S0tI4cOAAMLm8YGtrq8paxAa06paI8NRTT/HAAw+wbt260LatW7eSmZnJ888/b+FkIgK6wEwk7tXV1XHx4kXWrl0btj0vL08XmYnYhM6sRUREbE5n1iIiIjanshYREbE5lbWIiIjNqaxFRERsTmUtIiJicyprERERm1NZi4iI2JzKWkRExOb+B9puQA0ILvSEAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 576x396 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "            N    Z    A Element  Ebinding   Eapprox\n",
-      "A                                                  \n",
-      "1   0       0    1    1       H  0.000000  0.000000\n",
-      "2   1       1    1    2       H  1.112283  1.112283\n",
-      "3   2       2    1    3       H  2.827265  2.827265\n",
-      "4   6       2    2    4      He  7.073915  7.073915\n",
-      "5   9       3    2    5      He  5.512132  5.512132\n",
-      "6   14      3    3    6      Li  5.332331  5.332331\n",
-      "7   19      4    3    7      Li  5.606439  5.606439\n",
-      "8   24      4    4    8      Be  7.062435  7.062435\n",
-      "9   29      5    4    9      Be  6.462668  6.462668\n",
-      "10  34      6    4   10      Be  6.497630  6.497630\n",
-      "11  40      6    5   11       B  6.927732  6.927732\n",
-      "12  46      6    6   12       C  7.680144  7.680144\n",
-      "13  52      7    6   13       C  7.469849  7.469849\n",
-      "14  57      8    6   14       C  7.520319  7.520319\n",
-      "15  64      8    7   15       N  7.699460  7.699460\n",
-      "16  72      8    8   16       O  7.976206  7.976206\n",
-      "17  78      9    8   17       O  7.750728  7.750728\n",
-      "18  85     10    8   18       O  7.767097  7.773058\n",
-      "19  93     10    9   19       F  7.779018  7.773058\n",
-      "20  102    10   10   20      Ne  8.032240  8.032240\n",
-      "21  110    11   10   21      Ne  7.971713  7.971713\n",
-      "22  118    12   10   22      Ne  8.080465  8.080465\n",
-      "23  128    12   11   23      Na  8.111493  8.111493\n",
-      "24  137    12   12   24      Mg  8.260709  8.260709\n",
-      "25  146    13   12   25      Mg  8.223502  8.223502\n",
-      "26  154    14   12   26      Mg  8.333870  8.333870\n",
-      "27  164    14   13   27      Al  8.331553  8.331553\n",
-      "28  174    14   14   28      Si  8.447744  8.447744\n",
-      "29  183    15   14   29      Si  8.448635  8.448635\n",
-      "30  192    16   14   30      Si  8.520654  8.520654\n",
-      "...       ...  ...  ...     ...       ...       ...\n",
-      "238 3089  146   92  238       U  7.570125  7.573113\n",
-      "239 3099  146   93  239      Np  7.560567  7.558304\n",
-      "240 3109  146   94  240      Pu  7.556042  7.558304\n",
-      "241 3118  147   94  241      Pu  7.546439  7.546439\n",
-      "242 3127  148   94  242      Pu  7.541327  7.541327\n",
-      "243 3136  149   94  243      Pu  7.531008  7.527912\n",
-      "244 3144  150   94  244      Pu  7.524815  7.527912\n",
-      "245 3154  149   96  245      Cm  7.515767  7.513619\n",
-      "246 3162  150   96  246      Cm  7.511471  7.513619\n",
-      "247 3170  151   96  247      Cm  7.501931  7.499329\n",
-      "248 3177  152   96  248      Cm  7.496728  7.499329\n",
-      "249 3186  152   97  249      Bk  7.486040  7.482998\n",
-      "250 3194  152   98  250      Cf  7.479956  7.482998\n",
-      "251 3201  153   98  251      Cf  7.470500  7.470500\n",
-      "252 3209  154   98  252      Cf  7.465347  7.465347\n",
-      "253 3216  155   98  253      Cf  7.454829  7.452027\n",
-      "254 3224  156   98  254      Cf  7.449225  7.452027\n",
-      "255 3232  156   99  255      Es  7.437821  7.434800\n",
-      "256 3241  156  100  256      Fm  7.431780  7.434800\n",
-      "257 3248  157  100  257      Fm  7.422194  7.422194\n",
-      "258 3256  157  101  258      Md  7.409675  7.409675\n",
-      "259 3264  157  102  259      No  7.399974  7.399974\n",
-      "260 3275  154  106  260      Sg  7.342562  7.342562\n",
-      "261 3280  157  104  261      Rf  7.371384  7.371384\n",
-      "262 3289  156  106  262      Sg  7.341185  7.341185\n",
-      "264 3304  156  108  264      Hs  7.298375  7.298375\n",
-      "265 3310  157  108  265      Hs  7.296247  7.297260\n",
-      "266 3317  158  108  266      Hs  7.298273  7.297260\n",
-      "269 3338  159  110  269      Ds  7.250154  7.250154\n",
-      "270 3344  160  110  270      Ds  7.253775  7.253775\n",
-      "\n",
-      "[267 rows x 6 columns]\n",
-      "0.009883615646716182\n"
-     ]
-    }
-   ],
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
    "source": [
     "\n",
     "#Decision Tree Regression\n",
@@ -2825,101 +1992,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.7/site-packages/matplotlib/__init__.py:886: MatplotlibDeprecationWarning: \n",
-      "examples.directory is deprecated; in the future, examples will be found relative to the 'datapath' directory.\n",
-      "  \"found relative to the 'datapath' directory.\".format(key))\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  % self.max_iter, ConvergenceWarning)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x720 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
    "source": [
     "from sklearn.neural_network import MLPRegressor\n",
     "from sklearn.metrics import accuracy_score\n",
@@ -2970,25 +2047,7 @@
    ]
   }
  ],
- "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": "3.7.4"
-  }
- },
+ "metadata": {},
  "nbformat": 4,
  "nbformat_minor": 2
 }
diff --git a/doc/pub/How2ReadData/ipynb/ipynb-How2ReadData-src.tar.gz b/doc/pub/How2ReadData/ipynb/ipynb-How2ReadData-src.tar.gz
index 1823d4414..44fa9526c 100644
Binary files a/doc/pub/How2ReadData/ipynb/ipynb-How2ReadData-src.tar.gz and b/doc/pub/How2ReadData/ipynb/ipynb-How2ReadData-src.tar.gz differ
diff --git a/doc/pub/How2ReadData/pdf/How2ReadData-minted.pdf b/doc/pub/How2ReadData/pdf/How2ReadData-minted.pdf
index bc2461995..0bf967a68 100644
Binary files a/doc/pub/How2ReadData/pdf/How2ReadData-minted.pdf and b/doc/pub/How2ReadData/pdf/How2ReadData-minted.pdf differ
diff --git a/doc/src/How2ReadData/How2ReadData.do.txt b/doc/src/How2ReadData/How2ReadData.do.txt
index 53b452322..60b64ce20 100644
--- a/doc/src/How2ReadData/How2ReadData.do.txt
+++ b/doc/src/How2ReadData/How2ReadData.do.txt
@@ -814,6 +814,14 @@ the relative error (why would we prefer the MSE instead of the relative error?)
 \epsilon_{\mathrm{relative}}= \frac{\vert \hat{y} -\hat{\tilde{y}}\vert}{\vert \hat{y}\vert}.
 \]
 !et
+
+The squared cost function results in an arithmetic mean-unbiased
+estimator, and the absolute-value cost function results in a
+median-unbiased estimator (in the one-dimensional case, and a
+geometric median-unbiased estimator for the multi-dimensional
+case). The squared cost function has the disadvantage that it has the tendency
+to be dominated by outliers.
+
 We can modify easily the above Python code and plot the relative error instead
 !bc pycod
 import numpy as np
@@ -915,7 +923,7 @@ The MAE is defined as follows
 \text{MAE}(\hat{y}, \hat{\tilde{y}}) = \frac{1}{n} \sum_{i=0}^{n-1} \left| y_i - \tilde{y}_i \right|.
 \]
 !et
-Finally we present the 
+We present the 
 squared logarithmic (quadratic) error
 !bt
 \[
@@ -928,6 +936,13 @@ estimate is best to use when targets having exponential growth, such
 as population counts, average sales of a commodity over a span of
 years etc. 
 
+
+Finally, another cost function is the Huber cost function used in robust regression.
+It is less sensitive to outliers in data than the squared error cost function.
+A variant for classification is also sometimes used, a quantity we will meet later. 
+
+
+
 We will discuss in more
 detail these and other functions in the various lectures.  We conclude this part with another example. Instead of 
 a linear $x$-dependence we study now a cubic polynomial and use the polynomial regression analysis tools of scikit-learn.