diff --git a/doc/Programs/ANN/.ipynb_checkpoints/cnnkeras-checkpoint.py b/doc/Programs/ANN/.ipynb_checkpoints/cnnkeras-checkpoint.py
new file mode 100644
index 000000000..b813b78f1
--- /dev/null
+++ b/doc/Programs/ANN/.ipynb_checkpoints/cnnkeras-checkpoint.py
@@ -0,0 +1,138 @@
+# import necessary packages
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn import datasets
+
+
+# ensure the same random numbers appear every time
+np.random.seed(0)
+
+# display images in notebook
+plt.rcParams['figure.figsize'] = (12,12)
+
+
+# download MNIST dataset
+digits = datasets.load_digits()
+
+# define inputs and labels
+inputs = digits.images
+labels = digits.target
+
+# RGB images have a depth of 3
+# our images are grayscale so they should have a depth of 1
+inputs = inputs[:,:,:,np.newaxis]
+
+print("inputs = (n_inputs, pixel_width, pixel_height, depth) = " + str(inputs.shape))
+print("labels = (n_inputs) = " + str(labels.shape))
+
+
+# choose some random images to display
+n_inputs = len(inputs)
+indices = np.arange(n_inputs)
+random_indices = np.random.choice(indices, size=5)
+
+for i, image in enumerate(digits.images[random_indices]):
+    plt.subplot(1, 5, i+1)
+    plt.axis('off')
+    plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
+    plt.title("Label: %d" % digits.target[random_indices[i]])
+plt.show()
+
+from keras.utils import to_categorical
+from sklearn.model_selection import train_test_split
+
+# representation of labels
+labels = to_categorical(labels)
+
+# split into train and test data
+# one-liner from scikit-learn library
+train_size = 0.8
+test_size = 1 - train_size
+X_train, X_test, Y_train, Y_test = train_test_split(inputs, labels, train_size=train_size,
+                                                    test_size=test_size)
+
+import tensorflow as tf
+
+
+from keras.models import Sequential
+from keras.layers.convolutional import Conv2D
+from keras.layers.convolutional import MaxPooling2D
+from keras.layers import Flatten
+from keras.layers import Dense
+from keras.regularizers import l2
+from keras.optimizers import SGD
+
+def create_convolutional_neural_network_keras(input_shape, receptive_field,
+                                              n_filters, n_neurons_connected, n_categories,
+                                              eta, lmbd):
+    model = Sequential()
+    model.add(Conv2D(n_filters, (receptive_field, receptive_field), input_shape=input_shape, padding='same',
+              activation='relu', kernel_regularizer=l2(lmbd)))
+    model.add(MaxPooling2D(pool_size=(2, 2)))
+    model.add(Flatten())
+    model.add(Dense(n_neurons_connected, activation='relu', kernel_regularizer=l2(lmbd)))
+    model.add(Dense(n_categories, activation='softmax', kernel_regularizer=l2(lmbd)))
+    
+    sgd = SGD(lr=eta)
+    model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
+    
+    return model
+
+epochs = 100
+batch_size = 100
+input_shape = X_train.shape[1:4]
+receptive_field = 3
+n_filters = 10
+n_neurons_connected = 50
+n_categories = 10
+
+eta_vals = np.logspace(-5, 1, 7)
+lmbd_vals = np.logspace(-5, 1, 7)
+
+CNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)
+
+for i, eta in enumerate(eta_vals):
+    for j, lmbd in enumerate(lmbd_vals):
+        CNN = create_convolutional_neural_network_keras(input_shape, receptive_field,
+                                              n_filters, n_neurons_connected, n_categories,
+                                              eta, lmbd)
+        CNN.fit(X_train, Y_train, epochs=epochs, batch_size=batch_size, verbose=0)
+        scores = CNN.evaluate(X_test, Y_test)
+        
+        CNN_keras[i][j] = CNN
+        
+        print("Learning rate = ", eta)
+        print("Lambda = ", lmbd)
+        print("Test accuracy: %.3f" % scores[1])
+        print()
+
+# visual representation of grid search
+# uses seaborn heatmap, could probably do this in matplotlib
+import seaborn as sns
+
+sns.set()
+
+train_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
+test_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
+
+for i in range(len(eta_vals)):
+    for j in range(len(lmbd_vals)):
+        CNN = CNN_keras[i][j]
+
+        train_accuracy[i][j] = CNN.evaluate(X_train, Y_train)[1]
+        test_accuracy[i][j] = CNN.evaluate(X_test, Y_test)[1]
+
+
+fig, ax = plt.subplots(figsize = (10, 10))
+sns.heatmap(train_accuracy, annot=True, ax=ax, cmap="viridis")
+ax.set_title("Training Accuracy")
+ax.set_ylabel("$\eta$")
+ax.set_xlabel("$\lambda$")
+plt.show()
+
+fig, ax = plt.subplots(figsize = (10, 10))
+sns.heatmap(test_accuracy, annot=True, ax=ax, cmap="viridis")
+ax.set_title("Test Accuracy")
+ax.set_ylabel("$\eta$")
+ax.set_xlabel("$\lambda$")
+plt.show()
diff --git a/doc/Programs/DimRed/Bayes.ipynb b/doc/Programs/DimRed/Bayes.ipynb
new file mode 100644
index 000000000..d938f54e5
--- /dev/null
+++ b/doc/Programs/DimRed/Bayes.ipynb
@@ -0,0 +1,1520 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import GPy\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import scipy.stats as stats \n",
+    "import matplotlib.mlab as mlab\n",
+    "import seaborn as sns\n",
+    "from sklearn.decomposition import PCA\n",
+    "from sklearn.preprocessing import StandardScaler, MinMaxScaler, Normalizer, RobustScaler\n",
+    "from IPython.display import display\n",
+    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
+    "from sklearn.gaussian_process.kernels import RBF\n",
+    "from scipy.stats import norm\n",
+    "from scipy.stats import uniform"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Read csv file and get the outputs(features) and inputs\n",
+    "features = ['SN112EK42.5', 'SN112EK47.5', 'SN112EK52.5', 'SN112EK57.5', 'SN112EK62.5', 'SN112EK67.5', 'SN112EK72.5', 'SN112EK77.5', 'SN112EK82.5', 'SN112EK87.5', 'SN124EK42.5', 'SN124EK47.5', 'SN124EK52.5', 'SN124EK57.5', 'SN124EK62.5', 'SN124EK67.5', 'SN124EK72.5', 'SN124EK77.5', 'SN124EK82.5', 'SN124EK87.5', 'DREK12.5', 'DREK17.5', 'DREK22.5', 'DREK27.5', 'DREK32.5', 'DREK37.5', 'DREK42.5', 'DREK47.5', 'DREK52.5', 'DREK60.0', 'DREK70.0', 'DREK80.0', 'DREK87.5']\n",
+    "inputs = ['S0', 'L', 'ms', 'mv']\n",
+    "error_features = ['SN112EK42.5_Error', 'SN112EK47.5_Error', 'SN112EK52.5_Error', 'SN112EK57.5_Error', 'SN112EK62.5_Error', 'SN112EK67.5_Error', 'SN112EK72.5_Error', 'SN112EK77.5_Error', 'SN112EK82.5_Error', 'SN112EK87.5_Error', 'SN124EK42.5_Error', 'SN124EK47.5_Error', 'SN124EK52.5_Error', 'SN124EK57.5_Error', 'SN124EK62.5_Error', 'SN124EK67.5_Error', 'SN124EK72.5_Error', 'SN124EK77.5_Error', 'SN124EK82.5_Error', 'SN124EK87.5_Error', 'DREK12.5_Error', 'DREK17.5_Error', 'DREK22.5_Error', 'DREK27.5_Error', 'DREK32.5_Error', 'DREK37.5_Error', 'DREK42.5_Error', 'DREK47.5_Error', 'DREK52.5_Error', 'DREK60.0_Error', 'DREK70.0_Error', 'DREK80.0_Error', 'DREK87.5_Error'\n",
+    "]\n",
+    "df = pd.read_csv(\"C:/Users/danny/OneDrive/Desktop/bayesian_example/e120_bugfix_model_new_mv.csv\", usecols=features)\n",
+    "df2 = pd.read_csv(\"C:/Users/danny/OneDrive/Desktop/bayesian_example/e120_bugfix_model_new_mv.csv\", usecols=inputs)\n",
+    "df3 = pd.read_csv(\"C:/Users/danny/OneDrive/Desktop/bayesian_example/e120_exp_result.csv\", usecols=features)\n",
+    "df4 = pd.read_csv(\"C:/Users/danny/OneDrive/Desktop/bayesian_example/e120_exp_result.csv\", usecols=error_features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": true
+   },
+   "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>SN112EK42.5</th>\n",
+       "      <th>SN112EK47.5</th>\n",
+       "      <th>SN112EK52.5</th>\n",
+       "      <th>SN112EK57.5</th>\n",
+       "      <th>SN112EK62.5</th>\n",
+       "      <th>SN112EK67.5</th>\n",
+       "      <th>SN112EK72.5</th>\n",
+       "      <th>SN112EK77.5</th>\n",
+       "      <th>SN112EK82.5</th>\n",
+       "      <th>SN112EK87.5</th>\n",
+       "      <th>...</th>\n",
+       "      <th>DREK27.5</th>\n",
+       "      <th>DREK32.5</th>\n",
+       "      <th>DREK37.5</th>\n",
+       "      <th>DREK42.5</th>\n",
+       "      <th>DREK47.5</th>\n",
+       "      <th>DREK52.5</th>\n",
+       "      <th>DREK60.0</th>\n",
+       "      <th>DREK70.0</th>\n",
+       "      <th>DREK80.0</th>\n",
+       "      <th>DREK87.5</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.049740</td>\n",
+       "      <td>1.009370</td>\n",
+       "      <td>0.971587</td>\n",
+       "      <td>0.933543</td>\n",
+       "      <td>0.895236</td>\n",
+       "      <td>0.860123</td>\n",
+       "      <td>0.828202</td>\n",
+       "      <td>0.797841</td>\n",
+       "      <td>0.769038</td>\n",
+       "      <td>0.742806</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.25778</td>\n",
+       "      <td>1.28492</td>\n",
+       "      <td>1.31206</td>\n",
+       "      <td>1.33919</td>\n",
+       "      <td>1.36544</td>\n",
+       "      <td>1.39081</td>\n",
+       "      <td>1.42918</td>\n",
+       "      <td>1.47076</td>\n",
+       "      <td>1.50127</td>\n",
+       "      <td>1.51709</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.101460</td>\n",
+       "      <td>1.076550</td>\n",
+       "      <td>1.053560</td>\n",
+       "      <td>1.032650</td>\n",
+       "      <td>1.013830</td>\n",
+       "      <td>0.995491</td>\n",
+       "      <td>0.977648</td>\n",
+       "      <td>0.959740</td>\n",
+       "      <td>0.941767</td>\n",
+       "      <td>0.928342</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.23401</td>\n",
+       "      <td>1.26444</td>\n",
+       "      <td>1.29513</td>\n",
+       "      <td>1.32608</td>\n",
+       "      <td>1.36158</td>\n",
+       "      <td>1.40161</td>\n",
+       "      <td>1.45320</td>\n",
+       "      <td>1.51812</td>\n",
+       "      <td>1.59659</td>\n",
+       "      <td>1.65511</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.033870</td>\n",
+       "      <td>0.999617</td>\n",
+       "      <td>0.967117</td>\n",
+       "      <td>0.937034</td>\n",
+       "      <td>0.909368</td>\n",
+       "      <td>0.884840</td>\n",
+       "      <td>0.863449</td>\n",
+       "      <td>0.842654</td>\n",
+       "      <td>0.822455</td>\n",
+       "      <td>0.805631</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.25178</td>\n",
+       "      <td>1.27908</td>\n",
+       "      <td>1.30598</td>\n",
+       "      <td>1.33249</td>\n",
+       "      <td>1.36163</td>\n",
+       "      <td>1.39341</td>\n",
+       "      <td>1.43365</td>\n",
+       "      <td>1.48005</td>\n",
+       "      <td>1.51807</td>\n",
+       "      <td>1.54192</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.979704</td>\n",
+       "      <td>0.948603</td>\n",
+       "      <td>0.921646</td>\n",
+       "      <td>0.900123</td>\n",
+       "      <td>0.884032</td>\n",
+       "      <td>0.867298</td>\n",
+       "      <td>0.849921</td>\n",
+       "      <td>0.837539</td>\n",
+       "      <td>0.830154</td>\n",
+       "      <td>0.821927</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.23993</td>\n",
+       "      <td>1.26528</td>\n",
+       "      <td>1.29176</td>\n",
+       "      <td>1.31937</td>\n",
+       "      <td>1.34603</td>\n",
+       "      <td>1.37173</td>\n",
+       "      <td>1.40500</td>\n",
+       "      <td>1.44910</td>\n",
+       "      <td>1.48531</td>\n",
+       "      <td>1.50366</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.026250</td>\n",
+       "      <td>0.998031</td>\n",
+       "      <td>0.974097</td>\n",
+       "      <td>0.951106</td>\n",
+       "      <td>0.929057</td>\n",
+       "      <td>0.908939</td>\n",
+       "      <td>0.890750</td>\n",
+       "      <td>0.876159</td>\n",
+       "      <td>0.865163</td>\n",
+       "      <td>0.855486</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.21375</td>\n",
+       "      <td>1.23669</td>\n",
+       "      <td>1.26262</td>\n",
+       "      <td>1.29155</td>\n",
+       "      <td>1.32245</td>\n",
+       "      <td>1.35531</td>\n",
+       "      <td>1.40611</td>\n",
+       "      <td>1.47931</td>\n",
+       "      <td>1.54623</td>\n",
+       "      <td>1.59240</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 33 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   SN112EK42.5  SN112EK47.5  SN112EK52.5  SN112EK57.5  SN112EK62.5  \\\n",
+       "0     1.049740     1.009370     0.971587     0.933543     0.895236   \n",
+       "1     1.101460     1.076550     1.053560     1.032650     1.013830   \n",
+       "2     1.033870     0.999617     0.967117     0.937034     0.909368   \n",
+       "3     0.979704     0.948603     0.921646     0.900123     0.884032   \n",
+       "4     1.026250     0.998031     0.974097     0.951106     0.929057   \n",
+       "\n",
+       "   SN112EK67.5  SN112EK72.5  SN112EK77.5  SN112EK82.5  SN112EK87.5  ...  \\\n",
+       "0     0.860123     0.828202     0.797841     0.769038     0.742806  ...   \n",
+       "1     0.995491     0.977648     0.959740     0.941767     0.928342  ...   \n",
+       "2     0.884840     0.863449     0.842654     0.822455     0.805631  ...   \n",
+       "3     0.867298     0.849921     0.837539     0.830154     0.821927  ...   \n",
+       "4     0.908939     0.890750     0.876159     0.865163     0.855486  ...   \n",
+       "\n",
+       "   DREK27.5  DREK32.5  DREK37.5  DREK42.5  DREK47.5  DREK52.5  DREK60.0  \\\n",
+       "0   1.25778   1.28492   1.31206   1.33919   1.36544   1.39081   1.42918   \n",
+       "1   1.23401   1.26444   1.29513   1.32608   1.36158   1.40161   1.45320   \n",
+       "2   1.25178   1.27908   1.30598   1.33249   1.36163   1.39341   1.43365   \n",
+       "3   1.23993   1.26528   1.29176   1.31937   1.34603   1.37173   1.40500   \n",
+       "4   1.21375   1.23669   1.26262   1.29155   1.32245   1.35531   1.40611   \n",
+       "\n",
+       "   DREK70.0  DREK80.0  DREK87.5  \n",
+       "0   1.47076   1.50127   1.51709  \n",
+       "1   1.51812   1.59659   1.65511  \n",
+       "2   1.48005   1.51807   1.54192  \n",
+       "3   1.44910   1.48531   1.50366  \n",
+       "4   1.47931   1.54623   1.59240  \n",
+       "\n",
+       "[5 rows x 33 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = df.loc[:, features].values\n",
+    "#scaling the data\n",
+    "#y_scaler = MinMaxScaler().fit_transform(y)\n",
+    "#y_scaler = Normalizer().fit_transform(y)\n",
+    "#y_scaler = RobustScaler().fit_transform(y)\n",
+    "#y_scaler = StandardScaler().fit_transform(y)\n",
+    "pd.DataFrame(data = y, columns = features).head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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>PCA 1</th>\n",
+       "      <th>PCA 2</th>\n",
+       "      <th>PCA 3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.004585</td>\n",
+       "      <td>0.189818</td>\n",
+       "      <td>-0.017025</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.889633</td>\n",
+       "      <td>0.023971</td>\n",
+       "      <td>0.013887</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.174100</td>\n",
+       "      <td>0.061279</td>\n",
+       "      <td>-0.026724</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.011284</td>\n",
+       "      <td>-0.096832</td>\n",
+       "      <td>-0.013462</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.320008</td>\n",
+       "      <td>-0.111562</td>\n",
+       "      <td>0.062695</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      PCA 1     PCA 2     PCA 3\n",
+       "0  0.004585  0.189818 -0.017025\n",
+       "1 -0.889633  0.023971  0.013887\n",
+       "2 -0.174100  0.061279 -0.026724\n",
+       "3 -0.011284 -0.096832 -0.013462\n",
+       "4 -0.320008 -0.111562  0.062695"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#applying PCA on the outputs and reducing it from 33D to 3D\n",
+    "pca = PCA(n_components=3)\n",
+    "yPCA = pca.fit_transform(y)\n",
+    "PCADf = pd.DataFrame(data = yPCA, columns = ['PCA 1', 'PCA 2', 'PCA 3'])\n",
+    "PCADf.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.95664316 0.03929512 0.00237127]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Seeing how much each PCA takes into account\n",
+    "print(pca.explained_variance_ratio_) #it seems like the 3PCA account for 98% of the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1.05226638 1.00984326 0.96921742 ... 1.46182261 1.48709511 1.50213919]\n",
+      " [1.10304362 1.0782522  1.05524568 ... 1.52386133 1.59330332 1.64266425]\n",
+      " [1.03278436 0.99840936 0.96670162 ... 1.47629668 1.51377543 1.53713317]\n",
+      " ...\n",
+      " [0.95791579 0.90589328 0.85745636 ... 1.38459667 1.38846038 1.38896563]\n",
+      " [1.01619208 0.95436387 0.89447893 ... 1.3714765  1.37153888 1.37266782]\n",
+      " [0.99489351 0.96558251 0.94040069 ... 1.45655175 1.51113185 1.55003265]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#testing if the reverse pca works\n",
+    "G = np.dot(yPCA, pca.components_) + pca.mean_\n",
+    "print(G)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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>S0</th>\n",
+       "      <th>L</th>\n",
+       "      <th>ms</th>\n",
+       "      <th>mv</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>31.159</td>\n",
+       "      <td>43.5</td>\n",
+       "      <td>0.980</td>\n",
+       "      <td>0.865000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>35.073</td>\n",
+       "      <td>56.1</td>\n",
+       "      <td>0.700</td>\n",
+       "      <td>0.875000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>32.395</td>\n",
+       "      <td>66.9</td>\n",
+       "      <td>0.860</td>\n",
+       "      <td>0.855000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>28.275</td>\n",
+       "      <td>86.7</td>\n",
+       "      <td>0.972</td>\n",
+       "      <td>1.095001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>27.657</td>\n",
+       "      <td>79.5</td>\n",
+       "      <td>0.612</td>\n",
+       "      <td>0.975000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       S0     L     ms        mv\n",
+       "0  31.159  43.5  0.980  0.865000\n",
+       "1  35.073  56.1  0.700  0.875000\n",
+       "2  32.395  66.9  0.860  0.855000\n",
+       "3  28.275  86.7  0.972  1.095001\n",
+       "4  27.657  79.5  0.612  0.975000"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#extracting and looking at the inputs\n",
+    "x = df2.loc[:, inputs].values\n",
+    "xdf = pd.DataFrame(data = x, columns = ['S0', 'L', 'ms', 'mv'])\n",
+    "xdf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "GaussianProcessRegressor(alpha=1e-10, copy_X_train=True,\n",
+       "                         kernel=RBF(length_scale=1), n_restarts_optimizer=10,\n",
+       "                         normalize_y=False, optimizer='fmin_l_bfgs_b',\n",
+       "                         random_state=None)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#using sckitlearn to do the gaussian process\n",
+    "kernel = RBF()\n",
+    "gp = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=10)\n",
+    "gp.fit(x, yPCA)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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>S0</th>\n",
+       "      <th>L</th>\n",
+       "      <th>ms</th>\n",
+       "      <th>mv</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>67.435407</td>\n",
+       "      <td>96.000057</td>\n",
+       "      <td>1.060497</td>\n",
+       "      <td>0.897444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>52.220715</td>\n",
+       "      <td>40.647893</td>\n",
+       "      <td>0.619497</td>\n",
+       "      <td>0.998561</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>112.420615</td>\n",
+       "      <td>80.647919</td>\n",
+       "      <td>0.754445</td>\n",
+       "      <td>0.877165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>111.576292</td>\n",
+       "      <td>93.796187</td>\n",
+       "      <td>0.954161</td>\n",
+       "      <td>0.648290</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>102.520674</td>\n",
+       "      <td>56.963868</td>\n",
+       "      <td>0.880245</td>\n",
+       "      <td>1.001980</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           S0          L        ms        mv\n",
+       "0   67.435407  96.000057  1.060497  0.897444\n",
+       "1   52.220715  40.647893  0.619497  0.998561\n",
+       "2  112.420615  80.647919  0.754445  0.877165\n",
+       "3  111.576292  93.796187  0.954161  0.648290\n",
+       "4  102.520674  56.963868  0.880245  1.001980"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#creating new randomized inputs x_ similar to x\n",
+    "msv_min = min(x[:,2].min(),x[:,3].min())\n",
+    "msv_max = max(x[:,2].max(),x[:,3].max())\n",
+    "sl_min = min(x[:,0].min(),x[:,1].min())\n",
+    "sl_max = max(x[:,0].max(),x[:,1].max())\n",
+    "x_sl = np.random.uniform(sl_min,sl_max,94)\n",
+    "x_msv = np.random.uniform(msv_min,msv_max,94)\n",
+    "x_sl = x_sl.reshape(47,2)\n",
+    "x_msv = x_msv.reshape(47,2)\n",
+    "x_ = np.concatenate((x_sl,x_msv),axis=1)\n",
+    "x_df = pd.DataFrame(data = x_, columns =  ['S0', 'L', 'ms', 'mv'])\n",
+    "x_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(47,)\n",
+      "(47, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#using the gp model trained on the inputs(x) and the outputs(y) on x_ to predict y_pred and y_std\n",
+    "y_pred, y_std = gp.predict(x_, return_std=True)\n",
+    "#y_pred, y_cov= gp.predict(x_, return_cov=True) #should I inverse pca on y_cov?\n",
+    "y_preddf = pd.DataFrame(data = y_pred, columns =  ['PCA 1', 'PCA 2', 'PCA 3'])\n",
+    "y_preddf.head()\n",
+    "print(y_std.shape)\n",
+    "print(y_pred.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "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>SN112EK42.5</th>\n",
+       "      <th>SN112EK47.5</th>\n",
+       "      <th>SN112EK52.5</th>\n",
+       "      <th>SN112EK57.5</th>\n",
+       "      <th>SN112EK62.5</th>\n",
+       "      <th>SN112EK67.5</th>\n",
+       "      <th>SN112EK72.5</th>\n",
+       "      <th>SN112EK77.5</th>\n",
+       "      <th>SN112EK82.5</th>\n",
+       "      <th>SN112EK87.5</th>\n",
+       "      <th>...</th>\n",
+       "      <th>DREK27.5</th>\n",
+       "      <th>DREK32.5</th>\n",
+       "      <th>DREK37.5</th>\n",
+       "      <th>DREK42.5</th>\n",
+       "      <th>DREK47.5</th>\n",
+       "      <th>DREK52.5</th>\n",
+       "      <th>DREK60.0</th>\n",
+       "      <th>DREK70.0</th>\n",
+       "      <th>DREK80.0</th>\n",
+       "      <th>DREK87.5</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.007239</td>\n",
+       "      <td>0.972153</td>\n",
+       "      <td>0.940416</td>\n",
+       "      <td>0.911383</td>\n",
+       "      <td>0.885053</td>\n",
+       "      <td>0.860981</td>\n",
+       "      <td>0.839165</td>\n",
+       "      <td>0.819277</td>\n",
+       "      <td>0.801317</td>\n",
+       "      <td>0.784839</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.241723</td>\n",
+       "      <td>1.267337</td>\n",
+       "      <td>1.293529</td>\n",
+       "      <td>1.320298</td>\n",
+       "      <td>1.346925</td>\n",
+       "      <td>1.373411</td>\n",
+       "      <td>1.410665</td>\n",
+       "      <td>1.455663</td>\n",
+       "      <td>1.494245</td>\n",
+       "      <td>1.519706</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.007240</td>\n",
+       "      <td>0.972153</td>\n",
+       "      <td>0.940416</td>\n",
+       "      <td>0.911383</td>\n",
+       "      <td>0.885053</td>\n",
+       "      <td>0.860981</td>\n",
+       "      <td>0.839165</td>\n",
+       "      <td>0.819277</td>\n",
+       "      <td>0.801317</td>\n",
+       "      <td>0.784839</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.241723</td>\n",
+       "      <td>1.267337</td>\n",
+       "      <td>1.293529</td>\n",
+       "      <td>1.320298</td>\n",
+       "      <td>1.346925</td>\n",
+       "      <td>1.373411</td>\n",
+       "      <td>1.410665</td>\n",
+       "      <td>1.455663</td>\n",
+       "      <td>1.494245</td>\n",
+       "      <td>1.519706</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.007239</td>\n",
+       "      <td>0.972153</td>\n",
+       "      <td>0.940416</td>\n",
+       "      <td>0.911383</td>\n",
+       "      <td>0.885053</td>\n",
+       "      <td>0.860981</td>\n",
+       "      <td>0.839165</td>\n",
+       "      <td>0.819277</td>\n",
+       "      <td>0.801317</td>\n",
+       "      <td>0.784839</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.241723</td>\n",
+       "      <td>1.267337</td>\n",
+       "      <td>1.293529</td>\n",
+       "      <td>1.320298</td>\n",
+       "      <td>1.346925</td>\n",
+       "      <td>1.373411</td>\n",
+       "      <td>1.410665</td>\n",
+       "      <td>1.455663</td>\n",
+       "      <td>1.494245</td>\n",
+       "      <td>1.519706</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.007239</td>\n",
+       "      <td>0.972153</td>\n",
+       "      <td>0.940416</td>\n",
+       "      <td>0.911383</td>\n",
+       "      <td>0.885053</td>\n",
+       "      <td>0.860981</td>\n",
+       "      <td>0.839165</td>\n",
+       "      <td>0.819277</td>\n",
+       "      <td>0.801317</td>\n",
+       "      <td>0.784839</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.241723</td>\n",
+       "      <td>1.267337</td>\n",
+       "      <td>1.293529</td>\n",
+       "      <td>1.320298</td>\n",
+       "      <td>1.346925</td>\n",
+       "      <td>1.373411</td>\n",
+       "      <td>1.410665</td>\n",
+       "      <td>1.455663</td>\n",
+       "      <td>1.494245</td>\n",
+       "      <td>1.519706</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.007239</td>\n",
+       "      <td>0.972153</td>\n",
+       "      <td>0.940416</td>\n",
+       "      <td>0.911383</td>\n",
+       "      <td>0.885053</td>\n",
+       "      <td>0.860981</td>\n",
+       "      <td>0.839165</td>\n",
+       "      <td>0.819277</td>\n",
+       "      <td>0.801317</td>\n",
+       "      <td>0.784839</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.241723</td>\n",
+       "      <td>1.267337</td>\n",
+       "      <td>1.293529</td>\n",
+       "      <td>1.320298</td>\n",
+       "      <td>1.346925</td>\n",
+       "      <td>1.373411</td>\n",
+       "      <td>1.410665</td>\n",
+       "      <td>1.455663</td>\n",
+       "      <td>1.494245</td>\n",
+       "      <td>1.519706</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 33 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   SN112EK42.5  SN112EK47.5  SN112EK52.5  SN112EK57.5  SN112EK62.5  \\\n",
+       "0     1.007239     0.972153     0.940416     0.911383     0.885053   \n",
+       "1     1.007240     0.972153     0.940416     0.911383     0.885053   \n",
+       "2     1.007239     0.972153     0.940416     0.911383     0.885053   \n",
+       "3     1.007239     0.972153     0.940416     0.911383     0.885053   \n",
+       "4     1.007239     0.972153     0.940416     0.911383     0.885053   \n",
+       "\n",
+       "   SN112EK67.5  SN112EK72.5  SN112EK77.5  SN112EK82.5  SN112EK87.5  ...  \\\n",
+       "0     0.860981     0.839165     0.819277     0.801317     0.784839  ...   \n",
+       "1     0.860981     0.839165     0.819277     0.801317     0.784839  ...   \n",
+       "2     0.860981     0.839165     0.819277     0.801317     0.784839  ...   \n",
+       "3     0.860981     0.839165     0.819277     0.801317     0.784839  ...   \n",
+       "4     0.860981     0.839165     0.819277     0.801317     0.784839  ...   \n",
+       "\n",
+       "   DREK27.5  DREK32.5  DREK37.5  DREK42.5  DREK47.5  DREK52.5  DREK60.0  \\\n",
+       "0  1.241723  1.267337  1.293529  1.320298  1.346925  1.373411  1.410665   \n",
+       "1  1.241723  1.267337  1.293529  1.320298  1.346925  1.373411  1.410665   \n",
+       "2  1.241723  1.267337  1.293529  1.320298  1.346925  1.373411  1.410665   \n",
+       "3  1.241723  1.267337  1.293529  1.320298  1.346925  1.373411  1.410665   \n",
+       "4  1.241723  1.267337  1.293529  1.320298  1.346925  1.373411  1.410665   \n",
+       "\n",
+       "   DREK70.0  DREK80.0  DREK87.5  \n",
+       "0  1.455663  1.494245  1.519706  \n",
+       "1  1.455663  1.494245  1.519706  \n",
+       "2  1.455663  1.494245  1.519706  \n",
+       "3  1.455663  1.494245  1.519706  \n",
+       "4  1.455663  1.494245  1.519706  \n",
+       "\n",
+       "[5 rows x 33 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Reverse PCA on y_pred to look like y\n",
+    "Y = np.dot(y_pred, pca.components_) + pca.mean_\n",
+    "Ydf= pd.DataFrame(data = Y, columns =  features)\n",
+    "Ydf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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>SN112EK42.5</th>\n",
+       "      <th>SN112EK47.5</th>\n",
+       "      <th>SN112EK52.5</th>\n",
+       "      <th>SN112EK57.5</th>\n",
+       "      <th>SN112EK62.5</th>\n",
+       "      <th>SN112EK67.5</th>\n",
+       "      <th>SN112EK72.5</th>\n",
+       "      <th>SN112EK77.5</th>\n",
+       "      <th>SN112EK82.5</th>\n",
+       "      <th>SN112EK87.5</th>\n",
+       "      <th>...</th>\n",
+       "      <th>DREK27.5</th>\n",
+       "      <th>DREK32.5</th>\n",
+       "      <th>DREK37.5</th>\n",
+       "      <th>DREK42.5</th>\n",
+       "      <th>DREK47.5</th>\n",
+       "      <th>DREK52.5</th>\n",
+       "      <th>DREK60.0</th>\n",
+       "      <th>DREK70.0</th>\n",
+       "      <th>DREK80.0</th>\n",
+       "      <th>DREK87.5</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>SN112EK42.5</th>\n",
+       "      <td>0.000313</td>\n",
+       "      <td>0.000341</td>\n",
+       "      <td>0.000364</td>\n",
+       "      <td>0.000384</td>\n",
+       "      <td>0.000400</td>\n",
+       "      <td>0.000414</td>\n",
+       "      <td>0.000428</td>\n",
+       "      <td>0.000439</td>\n",
+       "      <td>0.000448</td>\n",
+       "      <td>0.000458</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.000031</td>\n",
+       "      <td>-0.000027</td>\n",
+       "      <td>-0.000015</td>\n",
+       "      <td>0.000005</td>\n",
+       "      <td>0.000031</td>\n",
+       "      <td>0.000063</td>\n",
+       "      <td>0.000118</td>\n",
+       "      <td>0.000197</td>\n",
+       "      <td>0.000295</td>\n",
+       "      <td>0.000373</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SN112EK47.5</th>\n",
+       "      <td>0.000341</td>\n",
+       "      <td>0.000381</td>\n",
+       "      <td>0.000415</td>\n",
+       "      <td>0.000446</td>\n",
+       "      <td>0.000472</td>\n",
+       "      <td>0.000497</td>\n",
+       "      <td>0.000520</td>\n",
+       "      <td>0.000541</td>\n",
+       "      <td>0.000559</td>\n",
+       "      <td>0.000578</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.000037</td>\n",
+       "      <td>-0.000030</td>\n",
+       "      <td>-0.000014</td>\n",
+       "      <td>0.000010</td>\n",
+       "      <td>0.000042</td>\n",
+       "      <td>0.000082</td>\n",
+       "      <td>0.000147</td>\n",
+       "      <td>0.000245</td>\n",
+       "      <td>0.000364</td>\n",
+       "      <td>0.000458</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SN112EK52.5</th>\n",
+       "      <td>0.000364</td>\n",
+       "      <td>0.000415</td>\n",
+       "      <td>0.000461</td>\n",
+       "      <td>0.000502</td>\n",
+       "      <td>0.000539</td>\n",
+       "      <td>0.000575</td>\n",
+       "      <td>0.000608</td>\n",
+       "      <td>0.000639</td>\n",
+       "      <td>0.000666</td>\n",
+       "      <td>0.000693</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.000042</td>\n",
+       "      <td>-0.000033</td>\n",
+       "      <td>-0.000013</td>\n",
+       "      <td>0.000015</td>\n",
+       "      <td>0.000053</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000175</td>\n",
+       "      <td>0.000291</td>\n",
+       "      <td>0.000431</td>\n",
+       "      <td>0.000538</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SN112EK57.5</th>\n",
+       "      <td>0.000384</td>\n",
+       "      <td>0.000446</td>\n",
+       "      <td>0.000502</td>\n",
+       "      <td>0.000554</td>\n",
+       "      <td>0.000602</td>\n",
+       "      <td>0.000647</td>\n",
+       "      <td>0.000691</td>\n",
+       "      <td>0.000731</td>\n",
+       "      <td>0.000766</td>\n",
+       "      <td>0.000802</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.000048</td>\n",
+       "      <td>-0.000035</td>\n",
+       "      <td>-0.000012</td>\n",
+       "      <td>0.000020</td>\n",
+       "      <td>0.000063</td>\n",
+       "      <td>0.000117</td>\n",
+       "      <td>0.000202</td>\n",
+       "      <td>0.000334</td>\n",
+       "      <td>0.000493</td>\n",
+       "      <td>0.000613</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SN112EK62.5</th>\n",
+       "      <td>0.000400</td>\n",
+       "      <td>0.000472</td>\n",
+       "      <td>0.000539</td>\n",
+       "      <td>0.000602</td>\n",
+       "      <td>0.000659</td>\n",
+       "      <td>0.000715</td>\n",
+       "      <td>0.000768</td>\n",
+       "      <td>0.000817</td>\n",
+       "      <td>0.000861</td>\n",
+       "      <td>0.000905</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.000052</td>\n",
+       "      <td>-0.000037</td>\n",
+       "      <td>-0.000011</td>\n",
+       "      <td>0.000025</td>\n",
+       "      <td>0.000073</td>\n",
+       "      <td>0.000133</td>\n",
+       "      <td>0.000227</td>\n",
+       "      <td>0.000374</td>\n",
+       "      <td>0.000552</td>\n",
+       "      <td>0.000684</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 33 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             SN112EK42.5  SN112EK47.5  SN112EK52.5  SN112EK57.5  SN112EK62.5  \\\n",
+       "SN112EK42.5     0.000313     0.000341     0.000364     0.000384     0.000400   \n",
+       "SN112EK47.5     0.000341     0.000381     0.000415     0.000446     0.000472   \n",
+       "SN112EK52.5     0.000364     0.000415     0.000461     0.000502     0.000539   \n",
+       "SN112EK57.5     0.000384     0.000446     0.000502     0.000554     0.000602   \n",
+       "SN112EK62.5     0.000400     0.000472     0.000539     0.000602     0.000659   \n",
+       "\n",
+       "             SN112EK67.5  SN112EK72.5  SN112EK77.5  SN112EK82.5  SN112EK87.5  \\\n",
+       "SN112EK42.5     0.000414     0.000428     0.000439     0.000448     0.000458   \n",
+       "SN112EK47.5     0.000497     0.000520     0.000541     0.000559     0.000578   \n",
+       "SN112EK52.5     0.000575     0.000608     0.000639     0.000666     0.000693   \n",
+       "SN112EK57.5     0.000647     0.000691     0.000731     0.000766     0.000802   \n",
+       "SN112EK62.5     0.000715     0.000768     0.000817     0.000861     0.000905   \n",
+       "\n",
+       "             ...  DREK27.5  DREK32.5  DREK37.5  DREK42.5  DREK47.5  DREK52.5  \\\n",
+       "SN112EK42.5  ... -0.000031 -0.000027 -0.000015  0.000005  0.000031  0.000063   \n",
+       "SN112EK47.5  ... -0.000037 -0.000030 -0.000014  0.000010  0.000042  0.000082   \n",
+       "SN112EK52.5  ... -0.000042 -0.000033 -0.000013  0.000015  0.000053  0.000100   \n",
+       "SN112EK57.5  ... -0.000048 -0.000035 -0.000012  0.000020  0.000063  0.000117   \n",
+       "SN112EK62.5  ... -0.000052 -0.000037 -0.000011  0.000025  0.000073  0.000133   \n",
+       "\n",
+       "             DREK60.0  DREK70.0  DREK80.0  DREK87.5  \n",
+       "SN112EK42.5  0.000118  0.000197  0.000295  0.000373  \n",
+       "SN112EK47.5  0.000147  0.000245  0.000364  0.000458  \n",
+       "SN112EK52.5  0.000175  0.000291  0.000431  0.000538  \n",
+       "SN112EK57.5  0.000202  0.000334  0.000493  0.000613  \n",
+       "SN112EK62.5  0.000227  0.000374  0.000552  0.000684  \n",
+       "\n",
+       "[5 rows x 33 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#obtaiting the covariance from y_pred, however this is incorrect\n",
+    "Ycov = Ydf.cov()\n",
+    "Ycovdf = pd.DataFrame(data= Ycov)\n",
+    "Ycovdf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " C:\\Users\\danny\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: MatplotlibDeprecationWarning:Support for passing a (n, 1)-shaped error array to errorbar() is deprecated since Matplotlib 3.1 and will be removed in 3.3; pass a 1D array instead.\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "operands could not be broadcast together with shapes (33,47) (33,) ",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-13-a4695423b236>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mYy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'r'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrorbar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mY_ex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myerr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0my_ex_std\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfill\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mYy\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdiag\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mYcov\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mYy\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdiag\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mYcov\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'k'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'$E_{c.m.}$ (MeV)'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'$R_{n/p}$ & $DR_{n/p}$'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: operands could not be broadcast together with shapes (33,47) (33,) "
+     ]
+    },
+    {
+     "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": [
+    "#plotting the y_predict based on energy E_c.m. for example the column SN112EK42.5 represent energy 42.5 \n",
+    "Yy = np.array([Y[:,0],Y[:,1],Y[:,2],Y[:,3],Y[:,4],Y[:,5],Y[:,6],Y[:,7],Y[:,8],Y[:,9],Y[:,10],Y[:,11],Y[:,12],Y[:,13],Y[:,14],Y[:,15],Y[:,16],Y[:,17],Y[:,18],Y[:,19],Y[:,20],Y[:,21],Y[:,22],Y[:,23],Y[:,24],Y[:,25],Y[:,26],Y[:,27],Y[:,28],Y[:,29],Y[:,30],Y[:,31],Y[:,32]])\n",
+    "y_ex_std =df4.loc[:,error_features].values\n",
+    "y_ex_std = y_ex_std.reshape(33,1)\n",
+    "Y_ex = df3.loc[:, features].values\n",
+    "Y_ex = Y_ex.reshape(33,1)\n",
+    "plt.plot(features,Yy, 'r')\n",
+    "plt.errorbar(features,Y_ex, yerr = y_ex_std)\n",
+    "plt.fill(features, Yy - np.sqrt(np.diag(Ycov)), Yy + np.sqrt(np.diag(Ycov)), alpha=0.5, color='k')\n",
+    "plt.xlabel('$E_{c.m.}$ (MeV)')\n",
+    "plt.ylabel('$R_{n/p}$ & $DR_{n/p}$')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$Like = exp(-\\sum \\frac{(y(x) - y_{exp})^2}{2\\sigma^2})$ \n",
+    "\n",
+    "$like = e^{-(y(x)-y_{exp})(YcovM)(y(x)-y_{exp}).T}$\n",
+    "\n",
+    "$YcovM = Ycov - Y_{experror}I$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(47,)\n"
+     ]
+    },
+    {
+     "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": [
+    "#creating the likelihood function\n",
+    "Y_exp = df3.loc[:, features].values\n",
+    "y_exp_std =df4.loc[:,error_features].values\n",
+    "covM = Ycov - y_exp_std*np.eye(33)\n",
+    "Z = Y-Y_exp\n",
+    "H = np.dot(Z,covM) #matmul?\n",
+    "J = np.dot(H,Z.T) #J = (Y-Y_exp)*covM*(Y-Y_exp).T, Should be inverse covM\n",
+    "like = np.exp(-sum(J))\n",
+    "#like = np.exp(-sum(((Y-Y_exp)**2)/(2*y_exp_std**2)))\n",
+    "plt.hist(like,5)\n",
+    "print(like.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$Posterior = \\frac{Like*S0*L*ms*mv}{\\sum(like*(S0+L+ms+mv)}\n",
+    "\\\\\n",
+    "\\\\\n",
+    "posterior \\propto Like*S0*L*ms*mv$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.387185\n"
+     ]
+    }
+   ],
+   "source": [
+    "N = 10000 #number of iterations\n",
+    "\n",
+    "#size of the steps\n",
+    "met_step = 1\n",
+    "met_step2 = 0.05\n",
+    "met_step3 = 0.01\n",
+    "met_accept = 0\n",
+    "\n",
+    "#empty arrays to store the data from the mcmc\n",
+    "S0 = np.zeros(N)\n",
+    "L = np.zeros(N)\n",
+    "ms = np.zeros(N)\n",
+    "mv = np.zeros(N)\n",
+    "lik = np.zeros(N)\n",
+    "mu = np.zeros(N)\n",
+    "\n",
+    "#this is the likelihood function that I derived in the above line\n",
+    "data = like\n",
+    "\n",
+    "#starting points\n",
+    "S0[0] = 30\n",
+    "L[0] = 70\n",
+    "mu[0] = 0.8\n",
+    "lik[0] = 0.9\n",
+    "mv[0] = 1\n",
+    "\n",
+    "#Here is the random walk\n",
+    "for i in range(N-1):\n",
+    "    if np.random.rand() > 0.5:\n",
+    "        S0_c = S0[i] + uniform(0,met_step).rvs()\n",
+    "        L_c = L[i] + uniform(0,met_step).rvs()\n",
+    "        mv_c = mv[i] + uniform(0,met_step2).rvs()\n",
+    "    else:\n",
+    "        S0_c = S0[i] - uniform(0,met_step).rvs()\n",
+    "        L_c = L[i] - uniform(0,met_step).rvs()\n",
+    "        mv_c = mv[i] - uniform(0,met_step2).rvs()\n",
+    "       \n",
+    "    mu_c = norm(mu[i],met_step3).rvs()    \n",
+    "    ms_i = norm(0.7, 0.05).pdf(mu[i])\n",
+    "    ms_c = norm(0.7, 0.05).pdf(mu_c)\n",
+    "    lik_i = norm(mu[i],1.4).pdf(data).prod()\n",
+    "    lik_c = norm(mu_c,1.4).pdf(data).prod()\n",
+    "    \n",
+    "    #Here we have set up the parameter range\n",
+    "    constraints = [0.6<=ms_c<=1,\n",
+    "                 0.6<mv_c<1.2,\n",
+    "                 25.7<S0_c<36,\n",
+    "                 32<=L_c<=120]\n",
+    "    if all(constraints):\n",
+    "        #the metroplois ratio\n",
+    "        met_r = lik_c*S0_c*L_c*ms_c*mv_c/lik_i*S0[i]*L[i]*ms_i*mv[i]\n",
+    "        if np.random.rand() < min(1,met_r):\n",
+    "            S0[i+1] = S0_c\n",
+    "            L[i+1] = L_c\n",
+    "            mu[i+1] = mu_c\n",
+    "            mv[i+1] = mv_c\n",
+    "            ms[i] = ms_c\n",
+    "            lik[i] = lik_c\n",
+    "            met_accept = met_accept +1\n",
+    "        else:\n",
+    "            S0[i+1] = S0[i]\n",
+    "            L[i+1] = L[i]\n",
+    "            mu[i+1] = mu[i]\n",
+    "            mv[i+1] = mv[i]\n",
+    "            ms[i] = ms_i\n",
+    "            lik[i] = lik_i\n",
+    "    else:\n",
+    "        S0[i+1] = S0[i]\n",
+    "        L[i+1] = L[i]\n",
+    "        mu[i+1] = mu[i]\n",
+    "        mv[i+1] = mv[i]\n",
+    "        ms[i] = ms_i\n",
+    "        lik[i] = lik_i\n",
+    "        \n",
+    "        \n",
+    "M = met_accept/N\n",
+    "print(M)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Using the MCMC sampling figure 2 can be created using the plots below\n",
+    "#plt.hist(S0,30, range = (10,40))\n",
+    "#plt.hist(L,30, range=(30,120))\n",
+    "#plt.hist(ms,30,range =(0.6,1))\n",
+    "#plt.hist(mv,100, range = (0.6,1.2))\n",
+    "#plt.hist2d(ms,mv)\n",
+    "#plt.hist2d(ms,L)\n",
+    "#plt.hist2d(ms,S0)\n",
+    "#plt.hist2d(mv,ms)\n",
+    "#plt.hist2d(mv,L)\n",
+    "#plt.hist2d(mv,S0)\n",
+    "#plt.hist2d(L,ms)\n",
+    "#plt.hist2d(L,mv)\n",
+    "#plt.hist2d(L,S0)\n",
+    "#plt.hist2d(S0,mv)\n",
+    "#plt.hist2d(S0,L)\n",
+    "#plt.hist2d(S0,ms)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " C:\\Users\\danny\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: RuntimeWarning:divide by zero encountered in true_divide\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "inf\n"
+     ]
+    },
+    {
+     "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": [
+    "#using the ms and mv sampling from mcmc, figure 3 can be created\n",
+    "fI = 1/ms - 1/mv\n",
+    "plt.hist(fI, 100, range=(-1,1))\n",
+    "print(fI.mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "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>S0</th>\n",
+       "      <th>L</th>\n",
+       "      <th>ms</th>\n",
+       "      <th>mv</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>70.000000</td>\n",
+       "      <td>1.079819</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>70.000000</td>\n",
+       "      <td>0.684094</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>30.788374</td>\n",
+       "      <td>70.141137</td>\n",
+       "      <td>0.737421</td>\n",
+       "      <td>1.023541</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>31.319453</td>\n",
+       "      <td>70.521572</td>\n",
+       "      <td>0.737421</td>\n",
+       "      <td>1.026640</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>31.319453</td>\n",
+       "      <td>70.521572</td>\n",
+       "      <td>0.737421</td>\n",
+       "      <td>1.026640</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          S0          L        ms        mv\n",
+       "0  30.000000  70.000000  1.079819  1.000000\n",
+       "1  30.000000  70.000000  0.684094  1.000000\n",
+       "2  30.788374  70.141137  0.737421  1.023541\n",
+       "3  31.319453  70.521572  0.737421  1.026640\n",
+       "4  31.319453  70.521572  0.737421  1.026640"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#reshaping the sampling from mcmc to look like the inputs\n",
+    "S0 = S0.reshape(N,1)\n",
+    "L = L.reshape(N,1)\n",
+    "ms = ms.reshape(N,1)\n",
+    "mv = mv.reshape(N,1)\n",
+    "SL = np.concatenate((S0,L),axis=1)\n",
+    "msv = np.concatenate((ms,mv),axis=1)\n",
+    "x_mcmc = np.concatenate((SL,msv),axis=1)\n",
+    "x_MCMC = pd.DataFrame(data = x_mcmc, columns =  ['S0', 'L', 'ms', 'mv'])\n",
+    "x_MCMC.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#using the trained gaussian process to predict a new y based on the mcmc sampling. This new y will be the posterior\n",
+    "y_mcmc = gp.predict(x_mcmc, return_std=True)\n",
+    "#Reverse PCA on y_mcmc to look like y\n",
+    "Y_mcmc = np.dot(y_mcmc, pca.components_) + pca.mean_\n",
+    "Y_mcmcdf= pd.DataFrame(data = Y_mcmc, columns =  features)\n",
+    "Y_mcmcdf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#posterior mean\n",
+    "Y_mcmc_mean = Y_mcmcdf[features].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#here figure 1 is created\n",
+    "#plotting the Y_mcmc based on energy E_c.m. for example the column SN112EK42.5 represent energy 42.5 \n",
+    "Yy_mcmc = np.array([Y_mcmc[:,0],Y_mcmc[:,1],Y_mcmc[:,2],Y_mcmc[:,3],Y_mcmc[:,4],Y_mcmc[:,5],Y_mcmc[:,6],Y_mcmc[:,7],Y_mcmc[:,8],Y_mcmc[:,9],Y_mcmc[:,10],Y_mcmc[:,11],Y_mcmc[:,12],Y_mcmc[:,13],Y_mcmc[:,14],Y_mcmc[:,15],Y_mcmc[:,16],Y_mcmc[:,17],Y_mcmc[:,18],Y_mcmc[:,19],Y_mcmc[:,20],Y_mcmc[:,21],Y_mcmc[:,22],Y_mcmc[:,23],Y_mcmc[:,24],Y_mcmc[:,25],Y_mcmc[:,26],Y_mcmc[:,27],Y_mcmc[:,28],Y_mcmc[:,29],Y_mcmc[:,30],Y_mcmc[:,31],Y_mcmc[:,32]])\n",
+    "plt.plot(features,Yy_mcmc, 'g')\n",
+    "plt.plot(features, Y_mcmc_mean, 'k')\n",
+    "plt.plot(features,Yy, 'r')\n",
+    "plt.errorbar(features,Y_ex, yerr = y_ex_std)\n",
+    "plt.xlabel('$E_{c.m.}$ (MeV)')\n",
+    "plt.ylabel('$R_{n/p}$ & $DR_{n/p}$')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
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+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
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+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
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+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/doc/Programs/DimRed/e120_bugfix_model_new_mv.csv b/doc/Programs/DimRed/e120_bugfix_model_new_mv.csv
new file mode 100644
index 000000000..25f169a95
--- /dev/null
+++ b/doc/Programs/DimRed/e120_bugfix_model_new_mv.csv
@@ -0,0 +1,48 @@
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+33.425,106.5,0.892,0.684999897,0.976449,0.93286,0.892331,0.855684,0.822918,0.792691,0.765001,0.739283,0.715539,0.691747,1.29326,1.25859,1.22585,1.1914,1.15523,1.12247,1.09311,1.06187,1.02875,0.996137,1.18384,1.20432,1.22706,1.25091,1.27588,1.30069,1.32534,1.34997,1.3746,1.39842,1.42281,1.43707,1.44012,0.00154,0.001627,0.001719,0.0018205,0.0019315,0.0020525,0.0021835,0.00232825,0.00248675,0.00265425,0.00190825,0.002047,0.002195,0.0023465,0.0025015,0.00267175,0.00285725,0.0030465,0.0032395,0.00344575,0.00152873,0.00161739,0.00179422,0.00201024,0.00226544,0.00255341,0.00287415,0.00323274,0.00362919,0.00427432,0.00527185,0.00644644,0.00746019
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diff --git a/doc/pub/How2ReadData/ipynb/How2ReadData.ipynb b/doc/pub/How2ReadData/ipynb/How2ReadData.ipynb
index ec0d96bce..4f41667fa 100644
--- a/doc/pub/How2ReadData/ipynb/How2ReadData.ipynb
+++ b/doc/pub/How2ReadData/ipynb/How2ReadData.ipynb
@@ -430,7 +430,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np"
+    "import nump\n",
+    "y as np"
    ]
   },
   {
@@ -442,13 +443,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 39,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0.62788768  0.95270524 -0.82962774  1.16046637 -0.31152511 -0.53939102\n",
+      "  0.54043276  0.14069506  1.23826241 -1.28557386]\n",
+      "0.9527052435093047\n"
+     ]
+    }
+   ],
    "source": [
     "n = 10\n",
     "x = np.random.normal(size=n)\n",
-    "print(x)"
+    "print(x)\n",
+    "print(x[1])"
    ]
   },
   {
@@ -463,7 +475,15 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "x = np.array([1, 2, 3])\n",
@@ -482,7 +502,15 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.38629436 1.94591015 2.07944154]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "x = np.log(np.array([4, 7, 8]))\n",
@@ -506,7 +534,15 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 1 2]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "from math import log\n",
@@ -528,7 +564,15 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.38629436 1.94591015 2.07944154]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "x = np.log(np.array([4, 7, 8], dtype = np.float64))\n",
@@ -544,12 +588,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.38629436 1.94591015 2.07944154]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
-    "x = np.log(np.array([4.0, 7.0, 8.0])\n",
+    "x = np.log(np.array([4.0, 7.0, 8.0]))\n",
     "print(x)"
    ]
   },
@@ -562,12 +614,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
-    "x = np.log(np.array([4.0, 7.0, 8.0])\n",
+    "x = np.log(np.array([4.0, 7.0, 8.0]))\n",
     "print(x.itemsize)"
    ]
   },
@@ -584,9 +644,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1.38629436 1.94591015 2.07944154]\n",
+      " [1.09861229 2.30258509 2.39789527]\n",
+      " [1.38629436 1.60943791 1.94591015]]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "A = np.log(np.array([ [4.0, 7.0, 8.0], [3.0, 10.0, 11.0], [4.0, 5.0, 7.0] ]))\n",
@@ -602,9 +672,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.38629436 1.09861229 1.38629436]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "A = np.log(np.array([ [4.0, 7.0, 8.0], [3.0, 10.0, 11.0], [4.0, 5.0, 7.0] ]))\n",
@@ -621,9 +699,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.09861229 2.30258509 2.39789527]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "A = np.log(np.array([ [4.0, 7.0, 8.0], [3.0, 10.0, 11.0], [4.0, 5.0, 7.0] ]))\n",
@@ -640,9 +726,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "n = 10\n",
@@ -660,14 +763,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 41,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
+      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "n = 10\n",
     "# define a matrix of dimension 10 x 10 and set all elements to one\n",
-    "A = np.ones( (n, n) )\n",
+    "A = np.eye( n )\n",
     "print(A)"
    ]
   },
@@ -680,9 +800,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.13883235 0.16578794 0.83591324 0.53093229 0.43828658 0.44147294\n",
+      "  0.81058042 0.90471242 0.75985306 0.65015412]\n",
+      " [0.5367777  0.62064869 0.5730995  0.44783891 0.55254031 0.22916951\n",
+      "  0.62502415 0.72544464 0.28186865 0.24079007]\n",
+      " [0.87309444 0.39639182 0.48916514 0.37108014 0.70819769 0.30059633\n",
+      "  0.50624688 0.97434081 0.08628836 0.07529887]\n",
+      " [0.93778677 0.80013962 0.75663638 0.52493405 0.91289002 0.62967827\n",
+      "  0.18833956 0.69572614 0.63064934 0.58702011]\n",
+      " [0.38917221 0.40771118 0.52341532 0.086439   0.41964853 0.82852196\n",
+      "  0.75017796 0.72808162 0.80634896 0.85284583]\n",
+      " [0.50492119 0.70610108 0.6424576  0.99407265 0.37489802 0.06411849\n",
+      "  0.49740309 0.64476141 0.80370131 0.20204962]\n",
+      " [0.99276881 0.97391374 0.10766979 0.76043228 0.38014282 0.33403001\n",
+      "  0.33157174 0.44297261 0.35781497 0.1575419 ]\n",
+      " [0.98529248 0.56354013 0.72044581 0.26099864 0.20825739 0.90771836\n",
+      "  0.54856735 0.31785249 0.7272262  0.38895366]\n",
+      " [0.74084311 0.31846284 0.34420921 0.43345144 0.05781962 0.35254955\n",
+      "  0.4309125  0.84639667 0.63585381 0.89812415]\n",
+      " [0.64935106 0.15417975 0.0916722  0.85895396 0.39864154 0.1850318\n",
+      "  0.76465137 0.4577945  0.075374   0.93877457]]\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "n = 10\n",
@@ -764,9 +911,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.20381774252138393\n",
+      "4.6822329078411125\n",
+      "0.4103425751132706\n",
+      "[[ 0.94483537  2.78661332  2.62703645]\n",
+      " [ 2.78661332  8.9863123   7.90574506]\n",
+      " [ 2.62703645  7.90574506 11.15801875]]\n",
+      "[18.86323713  0.06809913  2.15783016]\n"
+     ]
+    }
+   ],
    "source": [
     "# Importing various packages\n",
     "import numpy as np\n",
@@ -787,9 +948,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1. 0. 0. 0.]\n",
+      " [0. 1. 0. 0.]\n",
+      " [0. 0. 1. 0.]\n",
+      " [0. 0. 0. 1.]]\n",
+      "  (0, 0)\t1.0\n",
+      "  (1, 1)\t1.0\n",
+      "  (2, 2)\t1.0\n",
+      "  (3, 3)\t1.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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fc8OTPLBpoS6ueyyEoBNYGPG4Euia5dj7uWhZSErZFfrdC/wcZanJIEHs7RjAac+isjAnZu+pWle9I5PsbO8z+g7NwtiUn2PdIxcsy8WKD2ysxGIWvHK81+g7NAv7zwwCsH5hQUzft8ntpaXXB8D3dNJ3KBZCsBuoFUIsEUJYUCb7Fy4+SAiRD2wBno94ziqEsKt/A3cAh2MwJoM5srdjgI1VhVfc6Gw26l0OblnuxB+U3L6y1JiIZuDgmSECQRlTb0zlrfY+ghJK7Baj79As7D89gMMWWyNI9X7/5R7FI3v/xoW6qLCPWgiklH7gceC3wDHgx1LKI0KIbUKIbRGH3gv8Tko5GvFcKfCGEOIg8Efg11LK30Q7JoO50TM8QefAeFwmoia3N1zUpLalNriQfaeVZbn1i2JvkT7+g/28c005g2PTPPmBtbqYjBLNgdODrF9UEFMjSK2wv2ddBQW5mfgm/Lpo9RGTOgIp5Q4p5VIppUtK+bnQcw1SyoaIY/5HSnn/Ree1SynXhn5WqecaxBe1vF6ND2ysKoppMFediL720AYKczPZVFVkTEQzsP/0ANUlVgpyLTF9X3Uyumt1GdMBSUGuRReTUSIZGJ2i3TsacxFWK+yFENRVFnCwc1AXrT6MyuI0RC2v/+XBLrIyTAyPT8c0mBvuO1TjYHVFPj0jk8ZEdBFSSvadHoyLN6ZORmtC/5+HdDIZJZIDnUp8YF2M4wORrA1tYTk25Y/bZ8QKQwjSEDWY+7ujPRRbLXz8RwdiGsyN7DtUV5lPa88IGxYVGhMR572xjr4x+ken2LCoMG6ptRUFORRbLYYAz8D+04OYBNRVxlMICggEJUe6huP2GbHCEII0ZePiIqSMrtHcXFhTkY8/KDl+biQu7683VG/sR3uU0huzibil1gohWFOZbwhBiMiOo/tPD7C01E5z52Dc6lvqFir/pwdD2UlaxhCCNOWne88QlHDnqrK4ZpWsCVlchzq1/2VIBKo39u03TpJpFvzbiyfimlpbV1lAa++ILpYn4o0qwm+2ejlwZpAFBTlxrW9x2rMpz8/WhRAbQpCGNLm9fO7XxwD4mzuXxazHzUwsyM+myGrhkM76s8eTepeDgtxMpgOSD26Ob45/XUU+QQlHdbA8EW/CHUd/sI+RCT+72vviXt+yNhQw1jqGEKQhzZ1DXF/rINdipqrYGrPy+pkQQrC6wlieiOSNVi89w5OsqyyIe46/au0eNK4/oIjBpqoiAO5ZXxH3+pa6hfl09I0xODYV18+JFkMI0pBtW1z0j06xojwPU6i1QTyzSuoqlOwJPbXljRdNbi+P/WAfAB+8NnYdR2fDmZdNWV62sTQXosnt5fVWD2YBOw7Ft76lodFNRuj7pRpCWu25ZQhBGhIMSo51j7BqQV5CPk/dAe1Yt7E80dw5xMPXLgZgZXleXL0xlTWV+brbOjEeqPUt1SU2Vlfkx6zj6GzUVebztVeVSb+5c1DTPbcMIUhDTveP4Zv0s7I8MUKg3vhGnEDxxvwBSaZZUOO0AfH1xhoa3RTkZNLuGWV4YhrQrlUab5o7h3jqgfV0DY2zIgEifL4TKfxs31lNN/8zhCANUfOaVy1IjGVSnp9NsdXCIWOdGoCj3cPUOu1YMuL/9aurzOc3R84BcPjskKat0nizbYuLJSVWBsemWREyguJdaFfvclDrtHHSO6rp5n+GEKQhR7qGyDAJlpbZ4v5ZDY1u3mrvY01lftgjSFeLVOVo1zArE7QsV+9y8J/vXwvAU79v07RVmgjU7KlEXf8mt5fT/cre0d/bqd1OpGkhBJGFJCrpPBkd7R6mxmkjK8Mc989Sc7cLczNp7fXx6vHetLVIAXpHJvD6JhO2LAfwjlVl2LLMNLn7NG2VJgI1TrU8tBFQPFG9r7+6rRaAx2+p0WzPrbQQAnUyUv8D0tk9BmVpKJEW6VMPruelo70EgpKPPWtYpJA4ixSU+33SH6TYarSkPtY9wsKiHOzZmXH/LLXn1nvXKxs2moXQbM+ttBACdTL6i+/t5eFv7Upr97h3ZALPyGTC4gOgXP/3bVC+DBuritLyuqscDVmkKxLkEahGz12ryxkan+bL963TrFWaCI52DyfMG1N7bpXYsyjMzeT4uRHNNv9LCyEAZTJyOWy83urloU3p6x4fDQeKE2uR/vJgN5kmEd7QO1052jVMZWEO+Tnxt0jhvFV66wplkyBnXpZmrdJ4Mzbl51TfaMJEWEUIwfKyPE3324qJEAgh7hRCnBBCtAkhnpjh9ZuEEENCiAOhn0/P9dxY0eT20tqr/Ed8V8NBm3ihxknUjKEV5XkJiZOoFulTD61ndWU+1SVWwyJN4ESkWqXLy5TPPKFhqzTeHD83gpSJ88YiWV5u58S5EYLB2bZzTy5RC4EQwgw8jbL5/ErgASHEyhkOfV1KuS7083/neW5UqJPRE3ctB+DD1y9Ju8lIjZO81uJhYVEOR7qGEhInCe9N4HKwrNTOuaEJnnogfS3Sk97RhMYHVKpLrGSaBce6tWuVxptwfCYJQrCiLI/x6QCn+8cS/tlzIRYewSagLbTb2BTwLLA1AefOmXDQZkMlAALSzj1W4yS7T/VjMZsSFieJ3JtgWZmdgbFpapy2tLJIVW9MtUhXJsgbiyTTbMJVYuPEufSt7j7WPYw9OyOmexTPleXlSpbScY1e/1gIQQVwJuJxZ+i5i7lWCHFQCPGiEGLVPM+NCnUysmZlsKgolxM96eker60sICjB7UlOccuyMvXLkF5WqeqN/epgFwAT04GkZK0tL1OWJ9KVY93DrCjLi+kexXOl1mnHJNCsRxYLIZjpql68ELYPWCylXAv8N/CLeZyrHCjEo0KIPUKIPR6P54oHu7Q0fb8Mz+3rBOCda8qTkkaorlO39KTX9Ve9se27TmMxCz7zy6NJyVpbVpZH19AEQ2PTCf3cZKJ6Y8HQ5kgrFyTeGwPIsZipclhT2iPoBBZGPK4EuiIPkFIOSyl9ob93AJlCCMdczo14j2eklBullBtLSkqueLDLy+yc9I4y6U+vTphNbi9fePE4AP/7HfHdg2A2iqwWSuxZaecRgCIGhbmZTAVk0oq61CKqE2kkxKo39osDZxmbCmAxi6TVEK3QcOZQLIRgN1ArhFgihLAA9wMvRB4ghCgTIX9MCLEp9Ll9czk31iwts+MPSk56R+P5MZqjuXOIm5aVkJVhYlFRbkK6Xs7EsjT1yJralD0I1lTkJ62oS+vr1PFAvc8//fwRAJ7dfSZpNUTLy+x09I0xOqm93eKiFgIppR94HPgtcAz4sZTyiBBimxBiW+iwPwEOCyEOAl8B7pcKM54b7ZguxbLSkFWUZpPRti0uRiYDuEpsmBOwB8FsLCuz09IzQkCjaXTxoMnt5SPb9yGBD2ysTIo3BlCWl01edoZmrdJ4Ue9yhDOFPrh5cdJqiJaHxqBFjywmdQRSyh1SyqVSSpeU8nOh5xqklA2hv5+SUq6SUq6VUm6WUjZd6tx4ssShpNGlmxAAtPaMhAO2yWJZmZ1Jf5COvvTxyJo7h9h2kyK4taX2pHljamFTut37TW4vB84MkJ+TwbO7zyTFG2todDMW8gSOhwLGWup3ljaVxSqWDBPVDlvafRmGJ6bpHpqgtjT+HUcvRXidOo2u/7YtrvBOVUtDHmmystbUwiYp08MjU2uIHPYsrq4qTpo3VleZzz//6ijZmSZOnBvWXL+ztBMCUOIEWnTP4klr6N+71Jlcj6DWaUeI9EshbekZwWGzUGS1JG0MDY1uMs0C36SfzgGlNbKWrNJ40Nw5xJfvW0fv8CRLS21J88bUz/UHJC8d69Fcv7O0FILlZXY6B8bxaTBoEy9aenwASV8ayrGYqSq2pl0KaUuPL7wjWbKoq8znJ3uUFOIT50Y0Z5XGg21bXJTlZ+MPyrA3nCxvrN7lYHm5na7BCR7SWDvwtBQC1T1Pp8mopWeEnEwzFQWJr6qMpKHRTbHNcsHSUKpbpVJK2np94fsuWdS7HHzpvnUAfPONds1ZpfGiNWQE1SbZG25ye2n3KLExrW1Sk5ZCoK5Tt6TR8kRLzwi1pTZMpsRXVUZSV5nP4bPDnPSOMjEdSAurtHtoAt+kn9okCwHArStKsWdnsLO9P202qWnpGUEIkuqRqff5x0Ob1Dx2k0tT/c7STggaGt2c7hsj12IOxwlS3SIFZWki2RYpKFbpX9xYjQT+6YUjaWGVtoTjM8ldGgLlXp+YDuCwpc8mNW29PhYV5ZKdGf8d+WZD7Xd2zzqlg44lw6ypfmdpJwR1lfl89Nn9lOdnp8066cDoFJ4RJVimBe5euwCAH+0+kxZWqbo0kWwhVu/1O1aWMjzh5yv3JyeDJtG09IwkfVkocpOavOwMWnu11e8s7YRAjd53Doyz//RAWlmkWliaAOgeUjJWrlpcmBZWqZIxlEVhEjOG4LxVeuPSEqb8QRYW5WjKKo0HU/4gJ72jSU+bVhFCUOO00dbrS/ZQLiDthAAUMbi6qojx6SDv21CR0iIA0BK66ZZpQAia3F7+6tkDLCjIpshqSVpedyJp6fVpwhtTrdKakHXc1uvTlFUaDzr6RvEHpSauv0qt024IgRZocns5eGYQIGmVhomktWcEe1YG5fnZyR5K2CpdW1kQnohS2SqVUtLWM5L0ZaFI1KBpq8Ymo3jQopGMoUhqS214fVP0j04leyhh0k4I1HXSf7lnNQAf2LgwZS1StQXviXMj1JTaEEIkPTB+3iq10dGndIFNZav07OA4o1OBpNcQRJKfk4nTnqU5qzQetPYqGUOuEu1cf1foXtDS9U87IVAt0rvXLiAn04yUqbtbmdqC92j3MMtK7ZoKjNc4bQQlnPJqc+u+WKFa3VryCEC5/ungEbT2KBlDOZbkZQxdTG3YI9NO+nraCYFqkZpMguoSK22e1F0nrXc5+Py9qxmZ8HN2cFxTgXHVQtOSVRRLVG8s3Nqj1JZ0byySWqcNd68v5XsOtfaOhCderbAgP4dci1lT937aCUEkNaEvQypTkKtkqrze6tVUqqarxIYQqSsEqjf2RpuXEnsWR7uHNeONgXLv+yb9nBueSPZQ4sZ0QM0Y0pY3ZjJpL3MovYWgxKas4aZwz6GXjp4D4E/rqzSVqpljMVNZmKMp9ziWqEHwN9v6yMowacobAy7IHEo1VG+so2+U6YCk1qktbwyUuUetL9EC6S0EIZdR7f+RajS5vXx/52ksZhOffvdKzaVq1pRoyyqKNddWF2MS0DkwrilvDCIyhzQ0GcUK1Rv75cFuACamA5ryxgBqSm2cG55geEIb+0fHRAiEEHcKIU4IIdqEEE/M8PpDQojm0E+TEGJtxGunhBCHhBAHhBB7YjGeuaJ+Gdo8qWmVNncOsazMztIypceQ1lI1a5w22r2jKbtb2Y5D3UwHJFuWlmjKGwNw2CwU5GbS5kk9IVDv82deawfgP357QlPeGJxPZ9WKIRS1EAghzMDTwF3ASuABIcTKiw47CWyRUtYBnwWeuej1m6WU66SUG6Mdz3xYXGzFbBKa+c+INdu2uOjzTV2QOqelwHiN08aUP0jnQOplDjW5vTzx3CEA/uLGas15Y0IIxSNLQY8AlPt8YZHSaffhJG5PORu1GkshjYVHsAlok1K2SymngGeBrZEHSCmbpJQDoYc7gcoYfG7UWDJMLC7O1cx/RqwZm1KyhWo0lEMdSSqvUzd3DvG+Dcpt7nImb0OUS1FbaktJjwAUIXZ7RllUlKs5b6yh0U3nwDiWDFP43k92DCMWQlABnIl43Bl6bjY+DLwY8VgCvxNC7BVCPDrbSUKIR4UQe4QQezweT1QDjiSV16nV2IdLY+lzKqlc4bptiwspJbasDJz2LEBb3hgomVv9o1P0+SaTPZSY0uT28vj2/ZiF4LYVpZrzxuoq8/nYs/sptWfR2qONxpexEIKZGtzPuOgrhLgZRQj+NuLp66SUG1CWlh4TQtw407lSymeklBullBtLSkqiHXMYpcJ1jOlAMGbvqRXcIWtPS1WVkeTnZFKSwhWubR4fLqdS0a01Ghrd+EOxGa1YpbGiuXOIz2xdxVQgSI0GvTF1PD3Dk+zp0Ebjy1gIQSewMOJxJdB18UFCiDrgm4e2hPgAACAASURBVMBWKWWf+ryUsiv0uxf4OcpSU8KocdrwByUdfamXOeTu9WESUOXITfZQZiWVPTJ37yiuEmuyhzEjdZX5NLyqTPptHp8mrNJYsW2Li4KcTIDw9deaN1bvcrB+UQEjE37u27gw6TGMWAjBbqBWCLFECGEB7gdeiDxACLEIeA54WErZEvG8VQhhV/8G7gAOx2BMc6ZGY0GbWNLmUcrrszK0U14fSUOjG3t2xgUVrqlilarFWlr1xupdDr760AYAfrz7jCas0lgS9oY1uiza5PZypGsYgO27kr9tZdRCIKX0A48DvwWOAT+WUh4RQmwTQmwLHfZpoBj46kVpoqXAG0KIg8AfgV9LKX8T7ZjmQ3UKtzpw945qqtnZxdRV5vNGm5eRST+9I5MpZZWqFetaFQKA+hoHTnsWBzuHNFfnEC1tvT7yczIpTvIeEDOh3uf/8K4VAHyovirpMYyY1BFIKXdIKZdKKV1Sys+FnmuQUjaE/v4zKWVhKEU0nCYayjRaG/pZpZ6bKBoa3TR3DlKen51y66T+UHm9picil4NPhPZw/cKLx1PKKlUtUi0LcZPby+D4NPasDM1l1kSL2+PDVWLVZHwmvG3l+gqEgAyTSHoMI60ri9UKRIfNknLrpJ0D40wFgpoWAoCtoT1cf77/bEpZpW6PjwyTYHGxNuMz6r2+de0CRib9fPH9dUm3SmOJ26NdI0htfJmdqbRZcXtGkx7DSGshUKP3LT0+jneP8Pj21LNItbpGqtLW60MAdRX5KWWVtvX6WFScS6ZZm18x1Sq9ebkTgBJ7dtKt0lgxND6NZ2RS8/c+QLVDG40vtXmXJpB6l4NrXcX4g5L3rFuQEiIA52MeWi0mg5BV+sP9VDus5OVkai7fOxrcnlFNX3vVKlWt5nZv8q3SWNGu8bTpSFwlNtq9PoJJbrOS9kLQ5Payt0Mpev7Zvs6UmIRA8QgctizyczOTPZRZCW9bubAAtyd1tq2cDgTp6BvVhUW6uDgXIdCEVRor3KFCSi3HZ1RcTisT00G6hsaTOo60FgJ1nfTf3rsGgPeur9C9Raq24G3r9YVzqLUaAA9bpU4b3UMTjE76U8IqPd0/xnRA6sIizc40s7AwN7yUmAq4PT4yzYKFhTnJHsplCXtkSe6AnNZCoFqkd60px56VQTAFtq1UA+Anzo3gCvVh13oAvNqhCNZJb2oU9Z1PHdVmMdnFuEqsSZ+IYom710dVsZUMjcZnIlGFINlCrP0rFUdUi1QIZdvKdq/+t62sdzn43D2rGZ0KcKZ/TBcpmeoSSrK/DNGiemPuiB5PWvXGItHKOnWsUFJHte+NgdIO3J6dkfR7P62FIBJXiQ13b2pYRYVWbW5PORuLi3MxifNru3pF9cbeavfitGdx+OyQ5r0xUIoqtbBOHQuU+MwYLqc+vDEhhCbmHkMIQricyo5BvhTYtvKloz0APHLtYl2kZGZlmFlYpP916sjtKTPN2tuecjbUJSw9C/H57SnH8AeV+IwevDEIGaGGR6ANwuvUOv4ygBIY3r6zg0yT4J/uXqWblExXiS0l1qmvrS7GLODsoPa2p5wNV3jLVv0KseqN7TikbE85OunXhTcGSuZQ78gkI0ncttIQghDhL4NXv18GUALgy8vtuJza3J5yNqodVto9+l+n/s3hc0wFJDfWOnThjQEUWy3kaWCdOhrU+/xroY6qT77UogtvDLSROWQIQYjwOrXO86m3bXExMDZ9QQ67HgLgLqeNSX+Qs4P6Xaducnv5m581A/DhG7S3PeVsCCFwOZO/Th0t9S5HuKWHFrennA0tZA4ZQhAivE6t8xTGSb+SLeRy6CNYphJZ4apXmjuH+MBGZWsOV4lVN94YnM8c0jNq/UxlYY5uvDGARUW5mE3CEAKtUO2w6t4j6OgbIyi132PoYqrVgKWOr7/qdWVnmliQrxQz6cEbA0UIeoaTu04dDWq9jMUsuGlZiW68sYZGN3s6+llclBv2yJIR5DaEIAJXiY2T3lFdr1OrE2m1Q19CUGy1kJ+Tqet1alACrlXFVkwm7bU/vhSqEOs1YN/cOcTn713N2LTScVcv3pga5C7ItdDuTV4HZEMIIqgu0f86tbq0Uq2TqlYVJZ9a/xWubo8+egxF0tDoZmRc8QTU5SG9pF6qbNviojBXqZ9RN5vSgzemCtbR7iHaen08tn1fUoLcMRECIcSdQogTQog2IcQTM7wuhBBfCb3eLITYMNdzE8n5fGr9WqXuXh9ledlYszKSPZR5U62BfOpomPQH6BzQX3ymrjKfz+04FkqWGNVFW5KZUI0gvbT2UKl3ObjO5SAo4e665HRAjloIhBBm4GngLmAl8IAQYuVFh90F1IZ+HgW+No9zE8b5fGr9WqVu76huqiojaWh0k2ESF+RT680q1Wt8pt7l4OnQ/sUvHu7WTSHcxbh7fRfEZ/RCk9vL7lP9ADy3/2xS4hqx8Ag2AW2hbSengGeBrRcdsxX4rlTYCRQIIcrneG7C0Hs+tZSS9l6f7uIDoFilv2pWioHaPfq0SvUanwE19dKK2zOqm0K4i2n3jrLEYdNVfEa9z//z/WsB2LpuQVKC3LEQggrgTMTjztBzczlmLucCIIR4VAixRwixx+PxRD3oi2lodPNWex8u5/kKV71ZpB7fJCOTft25xqBMRJ+5W3EGn/pDmy6tUr3GZ0C517uHxjEJ+P5O/aReRuL2+HR37dUOyHesKqPIaiEQlEkJcsdCCGaS34vTbmY7Zi7nKk9K+YyUcqOUcmNJSck8h3h51Oi9LUvxCPRpkaoTkf4sUoCt6ysQKL2S9GiV6jU+o97rH9pcRVDCP969Qhepl5HotX5G7YAM59uBJyPIHQsh6AQWRjyuBLrmeMxczk0IavR+z6kBekcmkxa9jwY140NvVpHK7lP9CKF8IfRUEKSi1/iMapW+Y3UpAAU5Fl2kXkai1/hMJNWO5CVLxEIIdgO1QoglQggLcD/wwkXHvAB8KJQ9tBkYklJ2z/HchFHvcnBLaDPv21eW6koEQPEI9Bgsg/NW6bqFhZhNQjcFQSp6js+oVqk6dnXbUK2nXkaiNszT4/VXcTmt9I1OMTg2lfDPjloIpJR+4HHgt8Ax4MdSyiNCiG1CiG2hw3YA7UAb8A3gI5c6N9oxXSlNbi9vtCkTz68PdetmElJp9/p0FyxTUa3Sq5cUcso7xjVLinVlleo5PqNSaLVQZLXoMllCbaGtV28YiBDixGctxmQxU0q5A2Wyj3yuIeJvCTw213OTgWqR/vcD6/n//mc3t68o1U3AsqHRTV1lPu2e0XBMo8ntpblzSDdWnTrOzoFxpgJBOgfGqHc5NH/tVfQen1FxlVh1uS+B26PP+Ewkke3Ar1pcmNDPNiqLQ6gW6Y1LS1hclMukP6gbi7SuMp/Htu/jdP8Y1SX62Kd4NvRa1Kf3+IxKtcOmy30J2j36jM9EsrAwh0yzSIoQG0IQIjJ6r1a46mWdtN7l4Im7VgBw4tywbjyZmQi7xzppiRzepzgiPqO3tONIXE4rXt8UQ2P6aT4npVRSR3UcHwDIMJtYXGxNihAbQjADLqeVU94x/IFgsocyZ+zZikv82yP6TL1UKbRaKLZadNMSWU073nu6nyUOGztP9unWG4OI3vg6uf4AXt8UIxP6js+oVDusSfGGDSGYAVeJLbROrZ/mc3843gvAX26p1mXqZSTVJVbdeARq2vGhziH8gaCuvTE4H+PQQzvwsDemZgzpaJ/i2XA5bXT0jTGdYCPUEIIZ0MKOQfOhye3l+QNdOKwW/vauFbpLvbwYLWzmPR82LCokKKG116drbwzOr1PrYYMg1Rt76WgPAIPj07r2xkDxCPxByZn+sYR+riEEM+DSWW/25s4hKgpzWLEgD0A3vdhnw1ViS1o+9ZXw/IGzALxjVZnuvTF1nVoPHoF6n2/f2YHZBJ95/oiuvTE4nzmU6ICxIQQzUJCrrFPrxSp99IZqeoYnwp4M6KMX+2yo2R96SGNscnv57K+OAfCxW2t0742BmkKqj3u/3uWgND+bQBA+uFnf3hiAy3E+hTSRGEIwC3panjg3PMHYVEDX5fWRRFa4ap3mziHuXFWGEMq49e6NgXLvn+5P/Dr1ldDk9nKmf4zaUpvuvTGA/NxMHLbEG6GGEMyCy6mfwhr1pqnReTGTSmVhDhazSRdLc9u2uJgKBKkoyCHHYgb07Y2BEnSdDiR+nXq+NLm9PLZ9H0EJ716zQPfemBr8ri5JfAdkQwhmwVVio390ioFR7a9Tq+u5ei+oUckwm6hy5OrCIwBFiF0pIsIQWdSnbSFu7hzib96xHFDufb17Y+c7IJsT3gHZEIJZUL/Yeshnd3tGsWdnUGLLSvZQYkYyOzHOh2BQppQQNDS68foU40ddp9ZqSua2LS6sofqZGqd+9imeDVXIdrb3MzA2zUcS2AHZEIJZUFsF6CGfXZ2IhNBfs7nZcDmtnE5CPvV86RoaZ2I6GJ6I9E5dZT5/+7Nm8rIzdbEvh7vXh0lAVXFqeMP1Lge3rVDagd+6wpmw4LchBLNQWZiLxWzShVWaShYpKFZpUII/KDkdWqfWqlWqLp+kQlUrnLdKx6b8vN7q1XyBnNvjY2FRLtmZ5mQPJSY0ub00tig7MP7m0LmExTsMIZgFs0mwJEnl3vNhZGKanuHJlIkPgGKVbt/ZASgWn5at0vPxmdQR4nqXg+XldrqHJnhI4wVybb2pYwSp9/lXH9xAdqaJG5eWJCz4bQjBDKjR+8jMIa1bpKmSMQTKRPTkfesA+J+mU5q2Sts8PvJzMim2WpI9lJjR5PaGs1a+t7NDs1k4gaDkpHc0ZbwxtQPydbXKJkFjU4GEBb8NIZgBNXqflWHidP8Yr7V4DIs0wdy2opRci5kmd5+m2za4e33UOFMnPqNapR+7tRaAx2+u0WxK5tmBcSb9qROfieyAXOO00dabuA7IUQmBEKJICPGSEKI19PttuykIIRYKIf4ghDgmhDgihPiriNc+I4Q4K4Q4EPp5ZzTjiRXqOunvjvYQCEo++kPtWqRuj48Mk2BRUW6yhxJTmtxepvxBSvOyNF0o5PakjkUK563S96xdAEBWplmzKZnqsm2qLA1FUuO0cXZwnLEpf0I+L1qP4AngFSllLfBK6PHF+IFPSSlXAJuBx4QQKyNe/5KUcl3oJ+k7lanUuxy8u075MlxXU6xJEQDly7C4OJdMc+o4d6pVestyJ6OTAZ56QJuFQkNj03h9kyk1EalWaXl+NlaLGXcCrdL50tab2kIAiet3Fu3ssRX4Tujv7wD3XHyAlLJbSrkv9PcIyt7EFVF+btxpcnv53ZFzAPzhuEdzk5CKYpGm1hdBtUpvWFqCb9LPkhKrJq3SNrWiO0WWJiIRQlDjtNHaO5LsocyK2+Oj2GqhMIXiMyrqPdWWoOZ/0QpBqZSyG5QJH3Be6mAhRBWwHtgV8fTjQohmIcS3Z1paijj3USHEHiHEHo/HE+WwL41qkT790AYqC3NYv6hAUxapGsyeDgTp6BvF5dR/H/ZIVKtUDYC39mjTKk3lpQlQ4k6JmoiuhLZeX8rFxlSqiq2YTUI7QiCEeFkIcXiGn63z+SAhhA34GfBxKeVw6OmvAS5gHdANfHG286WUz0gpN0opN5aUlMzno+eNapHWuxzUOm0MjE1ryiJVg9kvHDjLdECCRLPB7GioLU2sVTRf3B4fFrOJysKcZA8lLtQ4bfQMTzI8oc1tK1OtfiYSS4aJxUW5Cbv3My53gJTyttleE0L0CCHKpZTdQohyoHeW4zJRRGC7lPK5iPfuiTjmG8Cv5jP4eBFpedY4bbzp7uOaJdqJE6jB7Ee/uxeA7bs6aHj4Ks2ML1YUWy0U5GbSqjEhaGh0U1eZj7t3lCpHLhlmE01uL82dQ5rzWqKhJmK3svWLZnXWk0Kfb5KBsemUXJZTcTkT12Yl2qWhF4BHQn8/Ajx/8QFCyav7FnBMSvnkRa+VRzy8Fzgc5XhiTq3TzpQ/SOeAtjox1rscrKlQPIAHNmk3vTIahBDUOm2a2yRF9ciOnB2iJrQsl5oemR3QpkeWahXdM1HjtHGqbzQhe6dHKwRfAG4XQrQCt4ceI4RYIIRQM4CuAx4GbpkhTfTfhRCHhBDNwM3AJ6IcT8ypKT2/Tq0lmtxe9nYMYMsy85O9nZqJX8QaLQYs610OvnTfWrqHJ/AMT2q64C0aFobagWtJCNT4WGTGUCrFxyKpCbUD70hAO/CohEBK2SelvFVKWRv63R96vktK+c7Q329IKYWUsu7iNFEp5cNSyjWh196jBp61hOp6aml5QrVAFxRkc9XiIt33Yb8UNU47A2PT9Pkmkz2UCyjNywZgd8eApgveoiHDbGKJw6opIVC9sddbPWRnmjjdN5aS3hgkNnModZLP40RedialeVmaskqbO4f4yv3r6R6aoNaZGrtizYYWhRjgV82KzfLApoWaLniLlhqnLZwmqwXUe/3lYz3YsjL46LOp6Y3B+W4BhhBohFqnXVNW0bYtLhYW5TDpD7I0tI6rxfTKWFCb4HzqudDk9vLN19sxCfjMe1altEfmcto40z/GxHQg2UMJU+9ykJVhwuubSllvDMCWlUF5fnZCYmSGEMwBte+HlDLZQwnTEopZqDGMVEWtcNWSEDR3DrG6Ip8qh5WsDHNKe2S1ThtBmbgK17nw8rEefJMBrq0uTllvTI2FRHpk8YyFGEIwB2pLlU6AXUMTyR5KGHWpqjaF0+dAyRzSWmHTti0u+n1TLHXaw8+lqkcWXqfWyPJQk9vLJ390AIAPX78kZb0xNRaSG2rz0dQW38w0QwjmwPkKV+3ECVp7fJTnZ2PPzkz2UOKO1jKHJqYDnOobZWmKe2MNjW56hicwifNLc8nO0GnuHOK+qxcCsLTUnrLemPrver3Vy+hUIO7bVhpCMAe0mE/d2jsSHleqo7UK13bPKEFJyl//usp8Pvnjg5TYszSzQdC2LS78QUlOpjlc0Z2q3li9y8Fdq8sAuHFpSVxjIYYQzIEiq4Viq0UztQTBoKSt15fyy0KgWKWBoBKb0YpVGl6WS3GPQLVK+0eneKtdO9tWtvb4qC21YTKlxh4Qs9Hk9vL740qzhpeO9cR1+csQgjmipTS6zgFlw/RUX5oAxSr95usnAUUItGCVtvSMhLcyTXXqXQ7qKgroH53mgU0Lky4CoFz/Wmdqe2ORjS9L87LYuLgwrrEQQwjmQEOjG1tWBq09I+HMoWRapS2hWEVNin8Z4LxVCvDsH09rwipt6fFRVZxLVkZqbJh+KZrcXo73KD0iv78z+Rk6g2NT9I5MprwRFNn4cmmpncE4N740hGAO1FXm81Z7H8MTfjwjk0m3StXiqlRfmlC5obYEh83CvtODmsgbb+v1hes3Uhn1Pv/Hdyn7SD28eVHSM3TUtOmlZal9/SO3rVxWaqelZ4RrlhTHLRZiCMEcqHc5+Phtyh6un99xPOlWaWvPCGV52eSlQcYQKBPS8IQfq8Wc9LzxiekAHX2jKR8ohvNW6b0bKjCbBEFJ0jN0VG84HYRYZVmZnUl/kNNx7DlkCMEcuXd9JQC/OHA26VZpa68vbbwB1Sr9kw0VjE4F+Lf3rkmqVer2+AhKUn5pAs5bpVkZZqodVlp6RpKeodPSM4ItK4MF+dlJG0OiWRbyfk6ci18KtSEEc6S1dwQBrCi3J8UqVSsNz2cM2ZOePZMIVKv0jlVKGp09JzOpVqmaOZZOFikok9HxOE5Ec6WlZ4TaUhtKd/v0oNZpRwhDCJKOapWuWpBHhsmUlGpGtdLwlwe7GJ8OYDal5q5kF6NapcvL8gDly5BMq7SlZ4QMk6CqOPUzhiJZXmanc2Ac36Q/qeNo7fFdUNGdDuRYzCwuyg0vi8UDQwjmgGqVbq4upqVnhE1VRQm3StXsmb//hbJ3z492n0l69kwiKc3LoiA3k+Pnhi9/cBxQPbKWHh9LHFYsGaa08MhUlkUIcbLw+ibpG51Km2XRSJaW2uN670clBEKIIiHES0KI1tDvGfezE0KcCm1Ac0AIsWe+5yebsFVansekP8ipvrGkWKXpsCvZbAghWFaavOUJ1SM7dHaQpaX2pGeOJZrlCVinvhyqRbwsxTOGZmJZmZ1TffHrAhutR/AE8IqUshZ4JfR4Nm4ObUqz8QrPTzrJ/jKou5LZszNSeley2VhRnkfLuRGCwcR3ga13OXjy/WvpGZ7E60vdXclmo6IgB6vFzIkkeGSqNxYZn0knbwwUIQgEZdy6wEYrBFuB74T+/g5wT4LPTyg1Thtmk0jK8oRqgRbbLFxbXZyyXRcvxbIyO6NTAToHxpPy+YVWCwC7TvYnPXMs0ZhMgtokeWSqN/Zai4e87Azcvb608sZAqSUAONETn7knWiEoVbeXDP12znKcBH4nhNgrhHj0Cs7XBNmZShrdse7EfxmaO4f44vvrODc8wYryvJTtungp1CWBZMUJftncBcAj1y5Oej1DMlheZudERHV9olDv9cYWD9asDB7/YXp5YwBVDiuZZsGJc/Fpc3NZIRBCvCyEODzDz9Z5fM51UsoNwF3AY0KIG+c7UCHEo0KIPUKIPR6PZ76nx4zl5XlJmYi2bXFRkGtBSmWJBFK36+JshK2iJFilTW4v33urg5xMU8rvSjYby8qUVgeekcTvH715STFCQPfQRNp5YwCZZhOuElvcluYuKwRSytuklKtn+Hke6BFClAOEfvfO8h5dod+9wM+BTaGX5nR+6NxnpJQbpZQbS0pK5vNvjClqGl0yWiKrnsiqBXkJ/2wtYM3KYFFRblKWJ5o7h1hcnMuaygKEEGnukSX++j9/4CzTAckty51p6Y01NLopslrCLTYgtv3Ool0aegF4JPT3I8DzFx8ghLAKIezq38AdwOG5nq811IBxSxK+DMe6h7FnZYT7sKcjSmFT4j2yR2+opnNgnJXl50U43Tyy5UlKIW1ye/nH548A8InblqalN1ZXmc/+04OcHRxnZGI65llr0QrBF4DbhRCtwO2hxwghFgghdoSOKQXeEEIcBP4I/FpK+ZtLna9llocmgmNJEIKj3cMsL7enVVXlxawos3PSO5rwzdQ7+scYmwqwMk29sYZGN8fPDVNizwp7BInK3GnuHOLWFU4yTILaUltaemP1LgeP36wYHf/8yyMxz1qLSgiklH1SylullLWh3/2h57uklO8M/d0upVwb+lklpfzc5c7XMgvys7FnZyQ8jS4YlBzvHr7AIk03GhrdCKE0P0v0JjVHu5T/73S9/mrmTlleFid6hhNaR7Fti4uh8WlqnDayM5XW3+nmjQG896pKlpba+One2Pc7MyqL54kQghVleRxPcObQmYExRqcC4UBxOlJXmc//NJ0ClHXqRE5GR7uHwhZpOqJa4a09Po51DfNYnPfQvZgjXcNp642pnPSO4vVN8bFbamIeJzGEYJ40NLpDrQ4Su0mNapGmsxDUuxw8/eAGALbv7EhoUdfRrmFqnLa02IxmNupdDm5cWkJAwl1ryhImAr0jE3hGJlm1IH3qBi5GNXqeenA9n7xjWczjJIYQzJO6ynzeaPPim/TTOTCeMKv0WPcwJpGe5fWRXF/rwGnPYv+ZxG5Sc7TbsEib3F52nVRWb5/f35WwYK1qBKVrthxcuGMZEPM4iSEE86Te5eBv71oOwBdeTNwmNUe7h6kuOb9Gmq40ub0MjU9jMYuEbZ3o9U3SMzyZtvEBOG+RfvXBDVgtZq6rcSQsc+eI4Q1fsGOZSizjJIYQXAHvv0rZpObXh7oTZpUe6x5J64kIzk9Gj1y7mKmA5B/evSIhk9Gx7lCg2LBIua7WwaqKfHpHJhOWuXO0a5iFRTnk56THjnzJwBCCK+DAmUHMQrCoKDeuxS1qs62hsWnODo6zojwv7ZptRaJORveEdoszm0RCJqN0zxiCCy3Suop8jnUPs6mqKCGZO0e7h1lVnr7xgURgCME8Ua3SG2qLGZmY5qkH4lfcoqbs/WTvmdAzMu2abUWiTkZLS21kZZho7hyKaxqhKsRHu4epKMihINeS1kKssqYyn0l/kNbe+PS9icQ36eekdzStvbFEYAjBPFGt0ttXlTEwNk1lYW7crFI1IPTkSy0AfL2xPe2abc1EhtnEqgV5HIqzJ6AK8Z5T/WFvLJ2FWEXdEyPe1x/OL8ulc6A4ERhCME9Uq3RtZQEABzsH42qV1rsc4ZYSH7p2cdqLgEpdZQGHu4YIxHFvgnqXgyc/sJazgxMMj0+n3R4Es1FVbMWWlcGhs/ETAtUbOxL6jFUL8g1vLI4YQnCFLCuzY8kw0dw5GNfPaXJ7aev14SqxpmWzrdmoq8xnbCqA2xPf5Ql7thKg/OOp9NuDYDZMJsHqijya4ygEqjf26oleiq0W2j3ptwdBIjGE4ArJNJtYWZ7HwTi6x01uLx/Zvo+ghPuuXpiWzbZmQ50Q4h0o/vm+TgA+fF2VIcQRrAkFjKcDwbi8v7os+npbH7lZ5rTcgyCRGEIQBesWFnD4bPyWJ5o7h/jw9UsAWL+oMC2bbc3GEocNq8UcV4+sye3lx3vO4LBZ+Me703MPgtlYU1nAlD8Y3kc4HqxbWEAwKDnTP254Y3HGEIIoUJcn2uKUPbFti4vxqQAZJsHqUHl9OjbbmgmzSbC6Ij+uotjcOYQ1KyNu1Zx6Rg0YH47j8tD2XR1I4D1rFxjeWJwxhCAK6iICxvHiwJlBVpTnkWNJ74rimairzOdo9zBT/vgsT7xn7QIGxqbZsKgg/JwhxEogt2tgHHv2+YBxrAO5TW4vT/6uFYDPbl1teGNxxhCCKKh2WLFnZXDwTHyEIBCUHDwzyPqIichAoaHRTVam+YLliVhPRvtODwCwYXFhzN4zFairzOejz+5nYWEuhzqH4pJW29w5xLIyG7VOG/m5mYY3FmcMIYgCk0mwpjJ+yxOtvSOMTgUMQS6BmwAAE7FJREFUIZiBusp8vvdWBwCHzsZnMtrXMUh2pimte9zMhDopt3t9HDo7FJeW1I/eUE27Z5SrIkTY8MbiR1RCIIQoEkK8JIRoDf1+m+kkhFgmhDgQ8TMshPh46LXPCCHORrz2zmjGk2gaGt0UW7M4fm44vGNWLK3S/acVT2P9QsMivZh6l4OvPbQBAXw/Ti2p950eoK6igEyzYS9dTL3LwS3LSwlKuHWFM+aBXLfHx/CE/wIhMIgf0d7hTwCvSClrgVdCjy9ASnlCSrlOSrkOuAoYQ9nAXuVL6utSyh0Xn69l6irz+cOJXqYDkmPdsd+1af/pAQpzM1lcnBuT90s16mscVBblcKRrOOZZJRPTAY50DbF+seGNzUST2xter/9187mYr93v6VCW5QwhSAzRCsFW4Duhv78D3HOZ428F3FLKjig/VxPUuxz863vXAPCll1pibpXuPz3I+kWFab1H8aVocnvxjkwB8N2dHTGdjA6fHWI6INmwyJiILibckvqhDSwrtVPjtMY8kLu3Y4Aiq4UlDmvM3tNgdqIVglIpZTdA6LfzMsffD/zwouceF0I0CyG+PdPSkooQ4lEhxB4hxB6PxxPdqGPI3WsXYM/O4LVWb0ys0nDH0fFpWnt9rF9YYJTWz4A6GT3xTmVviA9duzimk1E4UGwIwduI3CRlc3URbs8o/3X/upjGyvZ1DLDBMIISxmWFQAjxshDi8Aw/W+fzQUIIC/Ae4CcRT38NcAHrgG7gi7OdL6V8Rkq5UUq5saSkZD4fHVea3F6m/MGYbZSiltY/+8fTAFgyTEZp/Qyok9GDmxZhtZjp803FJKtEFeJ9HYMsKsqlxJ5lCPFFRLakvqa6mLGpANasjJgFcvt8k7R7R41loQRyWSGQUt4mpVw9w8/zQI8Qohwg9Lv3Em91F7BPStkT8d49UsqAlDIIfAPYFN0/J7GoVumf37CEqYDkE7fXRm2VqhkZX35ZyaFuaHQbpfUzoE5GmWYTVy8p4q32vphklahCvLO9jw2LCoyOo5dh05IiAHa190f9XmERDiVJbKwqNEQ4QUS7NPQC8Ejo70eA5y9x7ANctCykikiIe4HDUY4noahW6SP1ShuIkUl/TKzSepeDwlyl2dnDm42Oo5fj2upi2j2j9AxPRP1e9S4Hn7l7JYPj0wyMGR1HL4fDlkWt08auk31Rv5cqws8fOEumWTA66TdEOEFEKwRfAG4XQrQCt4ceI4RYIIQIZwAJIXJDrz930fn/LoQ4JIRoBm4GPhHleBKKapWW2LNYVmqnqS02VunLx3roGppg4+JCo7R+DqiT9M726CcjgIlppVK5scVj9LiZA9dUF7H7ZD/+KBvQqd7wbw6fo8hq4ZM/PmiIcIKISgiklH1SylullLWh3/2h57uklO+MOG5MSlkspRy66PyHpZRrpJR1Usr3qIFnPVJfU8zuU/3heoIrpcnt5ePPHgDgb+5cbpTWz4GVC/LIy87gLXdshOCne89gEvDRW2oMIZ4Dm6uLGZ0KhDeZj4a1lQUEpaRneNIQ4QRiVMrEiOtcDib9wXC2yZXS3DnEpiWF2LMyWL+owCitnwNmk2DTkmLeioFH8Hqrhz+eGuCmZU4+dccyQ4gvQ0OjG1MosUddHopmXf+br58kKOHe9RWGCCcQQwhixDXVRZhNgqa26Cajv7ixmuPdI1xX4whXtBql9ZemodFNeX42HX1jdA2OA1c+Ge04dA6A+69eCBgdRy9HXWU+//CLI5TnZ7OzvT+q4HqT28tXX20jJ9PEv/9JnSHCCcQQghixfddpljhyeTPipr2Sycjt8dE1NMGWZdpJkdU6dZX5PH/gLABvufuimozs2RlkmgXX1ZxfkjCEeHZUoewfneKNVg+Pb7/y4PrBM4NkZZi5dUUpmWaTIcIJxBCCGFFXmU/nwDgHzwwyMjF9xZPRqyeUYrkblxpCMFeUvkNXIYBvvtEeVabPH473cs2SYqxZGbEfaIpS73Jw83InUwHJzctLrnhdf3N1McMT09y2ovSC9zZEOP4YQhAj6l0OPnXHUoIS/s9zh654Mmps8VDjtFFRkBOnkaYm19U6cDltHOse4cFNVxZkPNM/Rmuvj5sMb2xeNLm97Grvw2yCXzV3X/FSzivHejEJjOufBAwhiCEPb64iwyT4VXP3vDIe1EKa8akAu072s2VpiVFIM0+a3F7ODSl1BN9569QVTUavtije2C3LL9cpxUBF9XyffmgD71hVRlaGmce3X9m6/ivHe9m4uIiCXEscRmpwKQwhiCH7Tg9gEgKLWfC9eTRBUwtp/l/TSab8QUrsWUYhzTxQJ6OnHlwfzraaa5BRFWFQloUWF+dybmjCEOE5Etl36N11CxiemOYvb6qe97r+2cFxjnUPc+sKQ4STgSEEMUKdjD51x1KmApKH59EETQ2K/dfLrZgFfN1oKzEv1MnopmVO7lxdxr6OQZ78wNo5TUaqCL96opcmt5flZXYe/6EhwnMlsu/Qzcuc5FrMtHtH57yurwrx748pnWduXVFqeMNJwBCCGKFORn92QzVledkcCj2eq2W0bmEBUkoC0mgrMV8iJ6Ot6yrwTfoZnQzMaTJSRfjxH+xnYjpIU1ufIcJXSI7FzG0rSnnx8Dmm51hlrArxT/d1UlWcS+/whOENJwFDCGKEOhmZTYJ7N1TwWquXGqdtzpbRl15qYSoged9VRiFNNFzrKsZpz+IXoXTSuVDvclBoVXo7fehaQ4SvlIZGNzVOG4Nj07zZpty/l7Pu610OPn/vag6eGaIgx8LjPzR6OyUDQwjiwPs2VBIISl440DWn45vavHz7zVMsLMzhP/9krVFIEwXfeL2dqxYX8uqJXobGpoHLT0Y/2n2GM/3jXFtdzA93nzGu+xVSV5nP/3vzJDmZpnD20Fys+6PdIwAc6Bw02kokCUMI4sDLx3pwlVj56d7O8HOXmoxePNxNICjZdpMLIYRRSBMFdZX5vNnmZTog2XH48pNRk9vLp58/TKZJ8NWHNhgiHAX1LgdPP7SBgIQXDpyd06b2Y1N+vvVGO5lmwceM3k5JwxCCOFBXmU/30ATHz41wpGtoxskoMltlZMKPPSuD8vzssFgYhTRXRr3LQcMHr8Ik4L9fab1sPcdOdx+BoORPNi6k0GoxRDhK6l0O3rm6jKmApK6y4LLW/b/uOM7oZIC/f9dKPmn0dkoahhDEgXqXgy/ftw6Av/7JwRknIzVI9pvD3ew4dI76mmL++ifNRpAsBtTXOLiuxkHX0ASblxS9bTKKFOFMswl/ULJhUYEhwjGgye3ltVYvlYU5vNbi4XdHzr3tGPX6TweCPH/gLMtL7SwttdHQ6DaEOEkYQhAn7lhVxlWLCjnWPcL1NY63TUbqDf+JHx1kKhBkZ3u/ESSLEU1uL4fPDuGwWXjxyDlePtZzweuqCL/W4uG7Ozuoq8znX188bohwlETWc3zrkasRAj76w7db9+r1f/KlFoYn/Ny9rvwCj9kQ4sRjCEGcaHJ7aff6yM0088LBrgsso8h4waRf2b/gESNbJSZEVrq+a005UsJj2/eFJ6Mmtzec6vvo9/biGZnkpGfUEOEYEFlctqzMzvqFBUz6g7x09LwQq9f/y/et45nGdoqtFr71xinj+ieZqIRACPF+IcQRIURQCLHxEsfdKYQ4IYRoE0I8EfF8kfj/2zv3GKmqO45/vuzyELuCPBQQLQ+NYhujSCk+sERIkdUoNrHR/lGs+IqhrUmbKtIYG9PWR2uaNi0WKJU2tNpWrY9KfFWwKfGx0GWVsgusYlmhu8sSlKpVYH/9457dXoY7zCw7d2aW+X2Sydx7zrlzfvd77j2/ex5zj/S8pC3h+6hYrTpeGX33skkA3LJyPQsfa2Dp35pZ8Lt/MOzYAcx/6HUAPj9umA+SFYh4ZTT7s6MYWN2Pj/d38qNnm7q1P2vsEOq37+leROjaC8Z5JVQA4v/n6NoX0RthF6/e2q3/qSdE3UAHzOj44BOfKVQGyMyO/GBpEtAJ/BL4tpnVJaSpAjYTLVXZArwOXGNm/5R0H7DbzO4JDuJ4M7stV75TpkyxurpDsiobHlzTzFljh3Rf3Lf9qYFH6rYz/Nj+dHywj6+dP46nNuyg44NPGDygiqXzIh/q6+MWnr82tnL9ijoGVvfjo32dzL9wHCNrBnHPqkYA5p59Ei9vaXfdU+J7T23k13/fxtBj+rPno33cMmMiL25qY3PrXoxoAZo1m13/YiFpnZkd8tDeK0cQ+/HVZHcE5wF3mdnssL8QwMx+KKkJmGFmO8NC9qvN7PRc+ZW7I8hk/4FOZj6whnc6PuwO6ycYVN2PZdd+rvsG6Go2e/9oYfnWH+t5dN2hfzBbOOcMbvrCxIP6tr0yKiydncb8FXW81NTWHVbVDzo74Y5Lz+CG6a5/McnmCIoxRnASsD223xLCAE7sWqc4fGd945SkGyXVSaprb29Pzdg0eG3bbvb+dz83XTSB4wZVc+4px9NpcP30CQdd+D5IVnjWNu/ipcZ2vnHxqQwd3J9zT4l6H+eePYabgtY+UyU9Xnm7gw0te/j6xadSM6iaccMHc6AT5p4zhhumu/7lQs7VNyS9AIxKiFpkZk/kkYcSwnrcDDGzJcASiFoEPT2+VGQ+7YyoGcAP/tLYvSbrtInD/SkoJTK1rzmmulv7NZvbWdu8q1v78yceOrPL6R2Z+h/n+pctOR2Bmc3qZR4twMmx/bFA17sXWiWNjnUNtR1ydB8nPni5tnkXi1e/xR2XnsGBTrhqylhvEqeIa19aXP++QzHGCKqJBotnAu8SDRZ/xcw2Srof6IgNFg8zs+/kyq+vjRF0kTmIDD4uUCxc+9Li+pcHqQwWS7oS+BkwEtgD1JvZbEljgGVmVhvS1QI/AaqA5Wb2/RA+HPgDcArwL+AqM9udK9++6ggcx3FKSaqzhoqNOwLHcZyeU8pZQ47jOE4Z447AcRynwnFH4DiOU+G4I3Acx6lw+uRgsaR24J0jPHwEUI5vd3O7eobb1TPcrp5RrnZB72z7tJmNzAzsk46gN0iqSxo1LzVuV89wu3qG29UzytUuSMc27xpyHMepcNwROI7jVDiV6AiWlNqALLhdPcPt6hluV88oV7sgBdsqbozAcRzHOZhKbBE4juM4MdwROI7jVDhHpSOQdJWkjZI6JU3JiFsoaaukJkmzsxw/TNLzkraE7+NTsPERSfXhs01SfZZ02yS9EdKl/qY9SXdJejdmW22WdJcEDbeGV4inbdf9kholNUh6XNLQLOmKoleu81fET0N8g6TJadkSy/NkSS9J2hSu/28mpJkh6b1Y+d6Ztl0h38OWS4n0Oj2mQ72k9yXdmpGmKHpJWi6pTdKbsbC86qGC3ItmdtR9gEnA6cBqYEos/ExgAzAQGA80A1UJx98H3B62bwfuTdneHwN3ZonbBowoonZ3Ea0tcbg0VUG7CcCAoOmZKdv1RaA6bN+brUyKoVc+5w/UAquIVuibBrxahLIbDUwO2zVE64Bk2jUDeLpY11O+5VIKvRLK9N9Ef7gqul7ARcBk4M1YWM56qFD34lHZIjCzTWbWlBB1BfCwmX1sZm8DW4GpWdKtCNsrgLnpWBo9CQFfBn6fVh4pMBXYamZvmdknwMNEmqWGmT1nZvvD7itEK92VinzO/wrgNxbxCjA0rMKXGma208zWh+29wCb+vz54uVN0vTKYCTSb2ZG+saBXmNnLQOZaLPnUQwW5F49KR3AYTgK2x/ZbSL5RTjSznRDdXMAJKdo0HWg1sy1Z4g14TtI6STemaEecBaF5vjxLczRfHdPiOqKnxySKoVc+519SjSSNA84BXk2IPk/SBkmrJH2mSCblKpdSX1NXk/1hrBR6QX71UEF0y7lmcbki6QVgVELUIjN7ItthCWGpzZ/N08ZrOHxr4AIz2yHpBOB5SY3h6SEVu4DFwN1EutxN1G11XeZPJBzbax3z0UvSImA/sDLLzxRcryRTE8Iyz7+o19pBGUufAh4FbjWz9zOi1xN1f/wnjP/8GTitCGblKpdS6jUAuBxYmBBdKr3ypSC69VlHYGazjuCwFuDk2P5YYEdCulZJo81sZ2ietqVho6L1nL8EnHuY39gRvtskPU7UFOxVxZavdpKWAk8nROWrY0HtkjQPuAyYaaGDNOE3Cq5XAvmcfyoa5UJSfyInsNLMHsuMjzsGM3tG0i8kjTCzVF+wlke5lESvwBxgvZm1ZkaUSq9APvVQQXSrtK6hJ4GrJQ2UNJ7Is7+WJd28sD0PyNbC6C2zgEYza0mKlHSspJqubaIB0zeT0haKjH7ZK7Pk9zpwmqTx4WnqaiLN0rTrEuA24HIz+zBLmmLplc/5Pwl8NcyGmQa819XMT4sw3vQrYJOZPZAlzaiQDklTieqAjpTtyqdciq5XjKyt8lLoFSOfeqgw92Lao+Gl+BBVYC3Ax0Ar8GwsbhHRKHsTMCcWvowwwwgYDrwIbAnfw1Ky8yHg5oywMcAzYXsC0SyADcBGoi6StLX7LfAG0BAuqNGZdoX9WqJZKc1FsmsrUV9offg8WEq9ks4fuLmrPIma7D8P8W8Qm72Wok0XEnULNMR0qs2wa0HQZgPRoPv5RbArsVxKrVfIdzBRxT4kFlZ0vYgc0U5gX6i75merh9K4F/0VE47jOBVOpXUNOY7jOBm4I3Acx6lw3BE4juNUOO4IHMdxKhx3BI7jOBWOOwLHcZwKxx2B4zhOhfM/a6jcSN2YLM4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "%matplotlib inline\n",
     "\n",
@@ -834,9 +1022,81 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "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"
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "from IPython.display import display\n",
@@ -861,9 +1121,81 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "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"
+    }
+   ],
    "source": [
     "data_pandas = pd.DataFrame(data,index=['Frodo','Bilbo','Aragorn','Sam'])\n",
     "display(data_pandas)"
@@ -878,9 +1210,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "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"
+    }
+   ],
    "source": [
     "display(data_pandas.loc['Aragorn'])"
    ]
@@ -894,9 +1240,89 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "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"
+    }
+   ],
    "source": [
     "new_hobbit = {'First Name': [\"Peregrin\"],\n",
     "              'Last Name': [\"Took\"],\n",
@@ -917,9 +1343,288 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
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+       "      <td>0.981321</td>\n",
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+       "    <tr>\n",
+       "      <th>1</th>\n",
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+       "      <td>0.221180</td>\n",
+       "      <td>-1.070043</td>\n",
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+       "      <td>0.255001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.458027</td>\n",
+       "      <td>0.435163</td>\n",
+       "      <td>-0.583595</td>\n",
+       "      <td>0.816847</td>\n",
+       "      <td>0.672721</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.104411</td>\n",
+       "      <td>-0.531280</td>\n",
+       "      <td>1.029733</td>\n",
+       "      <td>-0.438136</td>\n",
+       "      <td>-1.118318</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.618982</td>\n",
+       "      <td>1.541605</td>\n",
+       "      <td>-0.251879</td>\n",
+       "      <td>-0.842436</td>\n",
+       "      <td>0.184519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.937082</td>\n",
+       "      <td>0.731000</td>\n",
+       "      <td>1.361556</td>\n",
+       "      <td>-0.326238</td>\n",
+       "      <td>0.055676</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.222400</td>\n",
+       "      <td>-1.443217</td>\n",
+       "      <td>-0.756352</td>\n",
+       "      <td>0.816454</td>\n",
+       "      <td>0.750445</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>-0.455947</td>\n",
+       "      <td>1.189622</td>\n",
+       "      <td>-1.690617</td>\n",
+       "      <td>-1.356399</td>\n",
+       "      <td>-1.232435</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>-0.544439</td>\n",
+       "      <td>-0.668172</td>\n",
+       "      <td>0.007315</td>\n",
+       "      <td>-0.612939</td>\n",
+       "      <td>1.299748</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>-1.733096</td>\n",
+       "      <td>-0.983310</td>\n",
+       "      <td>0.357508</td>\n",
+       "      <td>-1.613579</td>\n",
+       "      <td>1.470714</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          0         1         2         3         4\n",
+       "0 -1.749765  0.342680  1.153036 -0.252436  0.981321\n",
+       "1  0.514219  0.221180 -1.070043 -0.189496  0.255001\n",
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+       "5  0.937082  0.731000  1.361556 -0.326238  0.055676\n",
+       "6  0.222400 -1.443217 -0.756352  0.816454  0.750445\n",
+       "7 -0.455947  1.189622 -1.690617 -1.356399 -1.232435\n",
+       "8 -0.544439 -0.668172  0.007315 -0.612939  1.299748\n",
+       "9 -1.733096 -0.983310  0.357508 -1.613579  1.470714"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0   -0.175300\n",
+      "1    0.083527\n",
+      "2   -0.044334\n",
+      "3   -0.399836\n",
+      "4    0.331939\n",
+      "dtype: float64\n",
+      "0    1.069584\n",
+      "1    0.965548\n",
+      "2    1.018232\n",
+      "3    0.793167\n",
+      "4    0.918992\n",
+      "dtype: float64\n"
+     ]
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+    {
+     "data": {
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+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
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+       "      <td>0.117430</td>\n",
+       "      <td>1.329492</td>\n",
+       "      <td>0.063724</td>\n",
+       "      <td>0.962990</td>\n",
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+       "    <tr>\n",
+       "      <th>1</th>\n",
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+       "      <td>0.048920</td>\n",
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+       "      <td>0.035909</td>\n",
+       "      <td>0.065026</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.209789</td>\n",
+       "      <td>0.189367</td>\n",
+       "      <td>0.340583</td>\n",
+       "      <td>0.667239</td>\n",
+       "      <td>0.452553</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.010902</td>\n",
+       "      <td>0.282259</td>\n",
+       "      <td>1.060349</td>\n",
+       "      <td>0.191963</td>\n",
+       "      <td>1.250636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2.621102</td>\n",
+       "      <td>2.376547</td>\n",
+       "      <td>0.063443</td>\n",
+       "      <td>0.709698</td>\n",
+       "      <td>0.034047</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.878123</td>\n",
+       "      <td>0.534362</td>\n",
+       "      <td>1.853835</td>\n",
+       "      <td>0.106431</td>\n",
+       "      <td>0.003100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.049462</td>\n",
+       "      <td>2.082875</td>\n",
+       "      <td>0.572069</td>\n",
+       "      <td>0.666597</td>\n",
+       "      <td>0.563167</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.207888</td>\n",
+       "      <td>1.415201</td>\n",
+       "      <td>2.858185</td>\n",
+       "      <td>1.839818</td>\n",
+       "      <td>1.518895</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.296414</td>\n",
+       "      <td>0.446453</td>\n",
+       "      <td>0.000054</td>\n",
+       "      <td>0.375694</td>\n",
+       "      <td>1.689345</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>3.003620</td>\n",
+       "      <td>0.966899</td>\n",
+       "      <td>0.127812</td>\n",
+       "      <td>2.603636</td>\n",
+       "      <td>2.162999</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          0         1         2         3         4\n",
+       "0  3.061679  0.117430  1.329492  0.063724  0.962990\n",
+       "1  0.264421  0.048920  1.144993  0.035909  0.065026\n",
+       "2  0.209789  0.189367  0.340583  0.667239  0.452553\n",
+       "3  0.010902  0.282259  1.060349  0.191963  1.250636\n",
+       "4  2.621102  2.376547  0.063443  0.709698  0.034047\n",
+       "5  0.878123  0.534362  1.853835  0.106431  0.003100\n",
+       "6  0.049462  2.082875  0.572069  0.666597  0.563167\n",
+       "7  0.207888  1.415201  2.858185  1.839818  1.518895\n",
+       "8  0.296414  0.446453  0.000054  0.375694  1.689345\n",
+       "9  3.003620  0.966899  0.127812  2.603636  2.162999"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -945,9 +1650,189 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
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+       "  <thead>\n",
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+       "      <th></th>\n",
+       "      <th>First</th>\n",
+       "      <th>Second</th>\n",
+       "      <th>Third</th>\n",
+       "      <th>Fourth</th>\n",
+       "      <th>Fifth</th>\n",
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+       "    <tr>\n",
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+       "      <td>-1.070043</td>\n",
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+       "      <td>0.816847</td>\n",
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+       "      <td>-0.531280</td>\n",
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+       "      <td>-1.118318</td>\n",
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+       "      <th>4</th>\n",
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+       "      <td>1.541605</td>\n",
+       "      <td>-0.251879</td>\n",
+       "      <td>-0.842436</td>\n",
+       "      <td>0.184519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.937082</td>\n",
+       "      <td>0.731000</td>\n",
+       "      <td>1.361556</td>\n",
+       "      <td>-0.326238</td>\n",
+       "      <td>0.055676</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.222400</td>\n",
+       "      <td>-1.443217</td>\n",
+       "      <td>-0.756352</td>\n",
+       "      <td>0.816454</td>\n",
+       "      <td>0.750445</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>-0.455947</td>\n",
+       "      <td>1.189622</td>\n",
+       "      <td>-1.690617</td>\n",
+       "      <td>-1.356399</td>\n",
+       "      <td>-1.232435</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>-0.544439</td>\n",
+       "      <td>-0.668172</td>\n",
+       "      <td>0.007315</td>\n",
+       "      <td>-0.612939</td>\n",
+       "      <td>1.299748</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>-1.733096</td>\n",
+       "      <td>-0.983310</td>\n",
+       "      <td>0.357508</td>\n",
+       "      <td>-1.613579</td>\n",
+       "      <td>1.470714</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      First    Second     Third    Fourth     Fifth\n",
+       "0 -1.749765  0.342680  1.153036 -0.252436  0.981321\n",
+       "1  0.514219  0.221180 -1.070043 -0.189496  0.255001\n",
+       "2 -0.458027  0.435163 -0.583595  0.816847  0.672721\n",
+       "3 -0.104411 -0.531280  1.029733 -0.438136 -1.118318\n",
+       "4  1.618982  1.541605 -0.251879 -0.842436  0.184519\n",
+       "5  0.937082  0.731000  1.361556 -0.326238  0.055676\n",
+       "6  0.222400 -1.443217 -0.756352  0.816454  0.750445\n",
+       "7 -0.455947  1.189622 -1.690617 -1.356399 -1.232435\n",
+       "8 -0.544439 -0.668172  0.007315 -0.612939  1.299748\n",
+       "9 -1.733096 -0.983310  0.357508 -1.613579  1.470714"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.08352721390288316\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 10 entries, 0 to 9\n",
+      "Data columns (total 5 columns):\n",
+      " #   Column  Non-Null Count  Dtype  \n",
+      "---  ------  --------------  -----  \n",
+      " 0   First   10 non-null     float64\n",
+      " 1   Second  10 non-null     float64\n",
+      " 2   Third   10 non-null     float64\n",
+      " 3   Fourth  10 non-null     float64\n",
+      " 4   Fifth   10 non-null     float64\n",
+      "dtypes: float64(5)\n",
+      "memory usage: 480.0 bytes\n",
+      "None\n",
+      "           First     Second      Third     Fourth      Fifth\n",
+      "count  10.000000  10.000000  10.000000  10.000000  10.000000\n",
+      "mean   -0.175300   0.083527  -0.044334  -0.399836   0.331939\n",
+      "std     1.069584   0.965548   1.018232   0.793167   0.918992\n",
+      "min    -1.749765  -1.443217  -1.690617  -1.613579  -1.232435\n",
+      "25%    -0.522836  -0.633949  -0.713163  -0.785061   0.087887\n",
+      "50%    -0.280179   0.281930  -0.122282  -0.382187   0.463861\n",
+      "75%     0.441264   0.657041   0.861676  -0.205231   0.923602\n",
+      "max     1.618982   1.541605   1.361556   0.816847   1.470714\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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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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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "df.columns = ['First', 'Second', 'Third', 'Fourth', 'Fifth']\n",
     "df.index = np.arange(10)\n",
@@ -978,9 +1863,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0  1  2  3]\n",
+      " [ 4  5  6  7]\n",
+      " [ 8  9 10 11]\n",
+      " [12 13 14 15]]\n",
+      "    0   1   2   3\n",
+      "0   0   1   2   3\n",
+      "1   4   5   6   7\n",
+      "2   8   9  10  11\n",
+      "3  12  13  14  15\n"
+     ]
+    }
+   ],
    "source": [
     "b = np.arange(16).reshape((4,4))\n",
     "print(b)\n",
@@ -1095,9 +1996,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 29,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x396 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Importing various packages\n",
     "import numpy as np\n",
@@ -1224,9 +2136,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 30,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ChQtVUVGhc845R2eccYYSEkJ6OwAA6AAhDePfcccduueee/Tdd99p9OjRTtUEAAAiKKRd840bN2rZsmX65ptvNHnyZKdqAgAAERRS2C9dulS//vWvddxxxzlVDwAAiLCgw/7FF19Ut27dtGrVKq1atUqSdNVVVzlWGAAAiIygw75fv36qqanxPHa5XI4UBAAAIivoE/QGDhyokpISDRs2TMOGDVNxcbGTdQEAgAgJes/+0Ucf1Ycffqi6ujoZY/TJJ584WRcAAIiQoMP+Zz/7mZqamjR06FDFxcXp4osvdrIuAAAQIUGHfcvwfVVVlaqrq9XQ0OBkXQAAIEJCuvRuwYIFWrVqldxut8rLy5Wfn+9UXQAAIEJCuoPenj17dPrpp+vJJ5/Ueeed51RNAAAggkIK+5SUFNXV1emf//ynPv30U6dqAgAAERTSMP5vf/tbxcfHKz8/X1deeaVTNQEAgAgK6dK7lhvpGGP03nvvaeDAgY4VBgAAIiOkS+8aGhq0efNm/fSnP9XevXudrAsAAERI0Mfshw0bprfeekuffvqphg0bpnfeecfJugAAQISEdMy+U6dOcrvdkqS0tDRHCgIAAJEV0tn4zc3N2rNnj9auXasdO3Y4VRMAAIigkMI+JydHycnJWr16tW688UanagIAABEU0jB+XV2drr/+eqdqAQAADghpz37FihVO1QEAABwS0p79e++9p7KyMh111FGSpKuuusqRogAAQOQEDPuXX35ZF1xwgSRpypQpjhcEAAAiK+Aw/jPPPCNJqqmp0bBhw9S3b1+99957GjZsmOPFAQCAtgsY9k1NTZKkCRMmSJI6d+6sNWvWOFsVAACImIBhn5KSoqKiIn377bfat2+fpP17+QAAIDYEPGZ/22236c4779R1112nm2++WT179tSRRx7ZHrUBAIAICBj2/fr18xy3P/nkk/XGG2/orrvucrwwAAAQGSFdenfcccfpuOOOc6oWAADggJBuqgMAAGIPYQ8AgOUIewAALEfYAwBgOcIeAADLEfYAAFiOsAcAwHIhXWcfrqKiIq1cuVJut1sul0vTpk1r9frChQu1e/dude/eXSUlJbrmmmt07LHHtkdpAABYz/Gwr62t1Zw5c1RQUKDExERNnz5da9asUXZ2tqdNTU2NZs+eLZfLpddff1333nuvHnvsMadLAwDgsOD4MP66deuUkZGhxMRESdLQoUNVWFjYqs2MGTPkcrkkSc3Nzdx7HwCACHJ8z76srEwpKSmex6mpqSorK/Patr6+Xvn5+ZozZ05Q005PT4tIjfCNPnYefdw+6Gfn0cfRy/Gwd7vdqq6u9jyuqqqS2+0+pF19fb1yc3N13XXXqU+fPkFNe9euvRGrE4dKT0+jjx1GH7cP+tl59LHz2vJlyvFh/CFDhmjbtm2qr6+XJBUXF2vEiBGqqKhQVVWVJGnfvn2aM2eOrrjiCg0cOFArVqxwuiwAAA4bju/ZJycnKzc3V3PnzlXXrl2VlZWl7Oxs5eXlqUuXLsrJydHMmTP15ZdfasuWLZL2n7A3atQop0sDAOCw4DLGmI4uIlwMGTmLYTnn0cftg352Hn3svKgexgcAAB2LsAcAwHKEPQAAliPsAQCwHGEPAIDlCHsAACxH2AMAYDnCHgAAyxH2AABYjrAHAMByhD0AAJYj7AEAsBxhDwCA5Qh7AAAsR9gDAGA5wh4AAMsR9gAAWI6wBwDAcoQ9AACWI+wBALAcYQ8AgOUIewAALEfYAwBgOcIeAADLEfYAAFiOsAcAwHKEPQAAliPsAQCwHGEPAIDlCHsAACxH2AMAYDnCHgAAyxH2AABYjrAHAMByhD0AAJYj7AEAsBxhDwCA5Qh7AAAsR9gDAGA5wh4AAMsR9gAAWI6wBwDAcoQ9AACWI+wBALAcYQ8AgOUIewAALEfYAwBgOcIeAADLEfYAAFiOsAcAwHKEPQAAliPsAQCwXLuEfVFRkXJzc7VgwQI99NBDXtu8/vrrOuOMM/T222+3R0kAABw2EpyeQW1trebMmaOCggIlJiZq+vTpWrNmjbKzsz1tNm/erG7duqlXr15OlwMAwGHH8T37devWKSMjQ4mJiZKkoUOHqrCwsFWb3r176+STT3a6FAAADkuO79mXlZUpJSXF8zg1NVVlZWURmXZ6elpEpgPf6GPn0cftg352Hn0cvRwPe7fbrerqas/jqqoqud3uiEx71669EZkOvEtPT6OPHUYftw/62Xn0sfPa8mXK8WH8IUOGaNu2baqvr5ckFRcXa8SIEaqoqFBVVZXTswcA4LDn+J59cnKycnNzNXfuXHXt2lVZWVnKzs5WXl6eunTpopycHBlj9Oijj2rr1q16/fXXlZCQoFNPPdXp0gAAOCy4jDGmo4sIF0NGzmJYznn0cfugn51HHzsvqofxAQBAxyLsAQCwHGEPAIDlCHsAACxH2AMAYDnCHgAAyxH2AABYjrAHAMByhD0AAJYj7AEAsBxhf4Ck/GXqOjxb3Xt1Vdfh2UrKX9bRJQEA0GaO/xBOrEjKX6bOkyd5HiesL1HnyZNUKalu3IUdVxgAAG3Env33jnzwfu/P/3leO1cCAEBkEfbfi//vFyE9DwBArCDsv9d0fF/vL8THc+weABDTCPvv1cy4wevzrro6dZ48icAHAMQswv57deMuVOXjT8kkJXl9nWP3AIBYRdgfoG7chVJjo9fXgj12z+V7AIBoQ9gfxNexe5/H9A/QcvlewvoSuZqaPJfvEfgAgI5E2B/E17H7mmuvD/heLt8DAEQjwv4gLcfuG/sPlElIUGP/gap8/KmgbqzD5XsAgGgU82HvxDHyunEX6rvCIu3eVq7vCouCvoNeWw4BAADglJgO+2g7Rt6WQwAAADglpsM+2o6Rt+UQAAAATonpH8KJxmPkdeMuJNwBAFElpvfsOUYOAEBgMR32sXKMnBvtAAA6UkwP49eNu1CV2n+MPv6/X6jp+L6qufb6qBpGbzmJsEXLSYSVUlTVCQCwV0yHvRT9x8j9nUQYzXUDAOwR08P4sSAaTyIEABxeCHuHcRIhAKCjEfYOi5WTCAEA9iLsD+DtrPm2nknPjXYAAB3NZYwxHV1EuHbt2uv1+aT8ZTrywft/OEN/xg0Bw/Xgs+b9OVzCOj09zWcfIzLo4/ZBPzuPPnZeenpa2O+1bs8+3Pvl+zpr3mtbfrIWABBDrAv7cO+XH8rZ8ZxJDwCIJdaFfbiXuoVydjxn0gMAYol1YR/upW6+zpr32pYz6QEAMcS6sA/3UjdfZ81zJj0AINbF/O1yI8nXrXcJdwBALLNuzz7cE/QAALCVdWHPvegBAGjNurDnXvQAALRmXdhzL3oAAFqzLuyj7V70wdxbv6333+9IgWrvqM8Wy30KAJFm5b3xo4Wv++0f+OUjmDYdJdC9rgPV3lGfzan5hvObC4FwP/H2EYl+dmL524R12XncGz8KJeUvU9o1V3t9Le2aqz17nCm33+a1zYFXD0TrXmqgKx866soIJ+Yb7m8uRLu2rFvRul46wdblj8MHYe+Alg2Dq67O6+uuujrPBiN+2xavbVquHojmjYzPKx8+/0xJ+cvCvjKirSHSlisyfM07lC8QsRKCbVm3onm9dEI0X9IbqfUtVtZbhIewd0Aov6DnS8vVAx29kfG3AfB1hYNLUufJk2Q6H+X1dX9XRoQbIgfWqQTv94oKdEWGv3kH+wUi5Q+z2hSgoW5s27KBbsu65e+9kQ6NaAihaL2kN1Jfujrqy1s0LNu2iKX6CXsHRGID0HL1QDgbGX8rYEgr59KlfjcAgX5PIO67cr+fzZtwAujgDZWvEZVAV2T4m3egSzqT8pfJnfUTHbnocZ/T8CfUjW1S/jJ1G9K/TRvotgSYz/d+sT6ioREtIwjReklvpHYGOmKnoiOWbSRPKI6WdTNYhL0DfG0AjMvlvX1mps+rB0LdyPhbAUNeOe+80+vTLRuAlisfQjnD0yQkKG3K733+IYUTQL42VMblkjngv0c+eL/fP0R/8/Z3SWdLv/r6cnPgtA/emGjpUr/nd/g6TNB58iSfh4CC3UC3JcB8tunkfVQl3NCI5AhCW0ZO4jes9/q6ty+QLV/Euv+os7r/qLO6ndjf8/cX6b3Ato44eD7f+hLv01lf0uaavX3uYNd5z3t7HKXuvdPVvWeXNtXhb/sX6vYx0BekqNvrNzHsf/+rjMp/ex5/yhjpkH/Vv5vs9fk9jz8V8rR8vaeh3wCv7Rv6D/T7mrdpmfh4r+2b4+KCmmcw/w7+HIFq3PP4U6ah3wDTHB9vGvoNMHsef8o0+6gz2Hm2TLc5KcnnvP0tU1/vC3Ya/v41x8UHvYw970lIOOSzHdxnex5/yjQenRny+hhovWx2uYKqKdh/zXFxPtfBUGo3zz3ns723/vH7GePiPMsz2H4JdV30Vo+315q6dvM63cajM8NehuHU/L//VbbaJnvq9LGc/K6/kmmOj/e5fno+Y0am1z7y1Ye+/m4aMzOD2vYcsm762j4mJPhfdwIsU3/rhImPDzsv2+XSu6KiIq1cuVJut1sul0vTpk1r9XpdXZ3uuece9ejRQ6WlpcrJydExxxwTcLrRfJlHUv4yHfnneT9cpnPt9Z7L0bw9H860vOneq6tcTU2HPG8SEiRjfL62e9uhe6Xpp58iffqp1/kEc/lgMBr7D9R3hUWex/4um5Pk9TWTkCBXY2PE53ngvI988H4l+Nj7CUa40zBJR2j35v+1eq57j6Pk8vNne+DnC2XZNB2dqerb7gj6crKUP8xS8jN/kerqpKQk1V52uRLff8/rZzy4z4Phr3aTlOT1cE1TZqbKiz8/5Hlf63JTZqbitxw6QuJvefn7LF2HZ4e0jJuOzlT5xz/UG87670ugS01DrbWFr8/fculdW7YHbRHokl8TFydXc7PP96ZN+X1I28duQ/p7HV0zCQkyaZ39jvS1zFPyvkz9bl/DjGzHw762tlZjxoxRQUGBEhMTNX36dE2YMEHZ2dmeNgsXLpTL5dLvf/97bdiwQbfffrueffbZgNOO5rDvKL7+gBv7D5SMCWnjlb6qQBo/3ut8vAXmgV9I6n9xis/j1wfy9ofk68tNuBunQPP0NV2TdIT2zn9EdeMu9PklKqj5uVzavXNPWNMwcXHavaOi1XPde6f7PC9Bar2hCKXPQglkXxvUmt9N9rrcw7nHgb/aA224D55Xeq+ukrcNufafUHqwxv4DFb9hfUgbf8n3l21/glle/v5+fQm0PMNdp33uHHwf9pH6Ow1Vy+f1/ffs/Qtiy3tD3T52O7G/4rd6P5QWbL3BzPOQzxNmZDt+zH7dunXKyMhQYmKiJGno0KEqLCxs1aawsFAnnniiJCkrK0tffPGFqqqqnC7NSv6OLYd8K+Hf/EYmzvsqcvAxwbpxF+q7wiLt3lau7wqLVH3nva3uZGiSkrxOx9ux34On1bIhDPY4pK9zI3zN0+d0mxoDnjsRjKZ+A8KeRlPf/oc+2dDgu/3Rma2CLpSTRUNp6+t4ZWLR+xG7g6WvekxcnJqy+vmuzdv5Af299GOAeYdzTkM4y/jAev0dgw/1xN9A7cNdpwO9L9wTlNu619kyX5/z9/N3E+i8HG/idmwPrUAv8wzmnIuIXfERiWPn/rz66qvm6quv9jx+4YUXzA033NCqzVlnnWU+//xzz+NTTz3VlJaWOl2avZ57zphBg4xJSNj/3+eeC+41b044wfsxs0GDQq/J23QCzT+YWg7+F+gY/sHzDOYz+qo/mH8t8wtnGt76x18/BPvZ2rpMffVxQkLw0wjE33Lx15feavDVvnfv0Ofhb50NZxkfWK+/zxzKsgxmeYa7Toe7zQj0z9eyCPXz+utDf8u7pU+C3T6G+zmDWaYHLruD24TJ8WH8NWvW6LHHHtOSJUskSYsXL9aOHTs0e/ZsT5sJEybo+uuv10knnSRp/97/6tWrlZqa6mRpAAAcFhwfxh8yZIi2bdum+vp6SVJxcbFGjBihiooKz1D9iBEj9PHHH0uSNmzYoL59+xL0AABESLucjf/+++9rxYoV6tq1qzp16qRp06YpLy9PXbp0UU5Ojvbt26d77rlH6enp+vbbbzV58uSgzsYHAACBxfSv3v4qNQoAAAS3SURBVAEAgMC4gx4AAJYj7AEAsJz3G1lHEafuvocfBOrjhQsXavfu3erevbtKSkp0zTXX6Nhjj+2gamNToD5usXz5cs2aNUvFxcVKSUlp5ypjW6A+NsbomWeekSRt3bpVlZWVuuuuuzqi1JgWqJ83b96svLw8nXDCCVq/fr3OPfdcnX766R1UbWzatWuXHnzwQX3xxRd66aWXDnk9rNwL+6K9dlBTU2POOOMMU1dXZ4wxZtq0aaaoqKhVm8cff9wsXLjQGGPMF198YcaPH9/udcayYPr4gQceMM3NzcYYYwoKCszkyZPbvc5YFkwfG2PMV199ZebNm2eOP/54U1VV1d5lxrRg+jg/P9/k5+d7Hq9fv75da7RBMP182223mcWLFxtjjCkpKTFnnnlme5cZ89544w2zatUqM27cOK+vh5N7UT2Mz933nBdMH8+YMUOu7+9K19zcrCOPPLK9y4xpwfRxbW2tFi1apKlTp3ZAhbEvmD5+9dVXVVFRoaefflrz5s1j5CQMwfRz9+7dVV6+/3a65eXlGjBgQHuXGfPOPvtsv+tnOLkX1WFfVlbW6gOnpqaqrKws5DbwLZT+q6+vV35+vmbMmNFe5VkhmD5+4IEHNGXKFM9GFKEJpo+3bdumqqoqTZw4UePGjdPvfvc7NYX5eweHq2D6+YorrtAnn3yiu+66Sw8//LAuuOCC9i7TeuHkXlQfs3e73aqurvY8rqqqktvtDrkNfAu2/+rr65Wbm6vrrrtOffr0ac8SY16gPt6+fbsqKyv1xhtveJ5bvHixhg8frhNOOKFda41VwazHqampGjx4sCTpmGOOUVVVlbZv367MzMx2rTWWBdPPN998sy666CKde+65Ki8v11lnnaW33npLXbp0ae9yrRVO7kX1nj1333NeMH28b98+zZkzR1dccYUGDhyoFStWdGTJMSdQH/fq1Ut33323cnJylJOTI2n/3hFBH7xg1uPs7Gxt3rxZ0v6NY1NTk9LT0zus5lgUTD9v377d06+dO3dWXFycmn38QiGC19bci/qb6nD3PecF6uNp06bpyy+/1I9+9CNJUk1NjdczROFboD6W9h/fXLp0qf785z9rypQp+s1vfqMePXp0cOWxI1Af7927V/fee68yMjL07bffatSoURo+fHhHlx1zAvXzv/71Lz399NPq37+/tmzZogEDBmi8j5/Khndr167VK6+8onfffVfjx4/XpEmTNH/+/DblXtSHPQAAaJuoHsYHAABtR9gDAGA5wh4AAMsR9gAAWI6wBwDAcoQ9AACWI+wBALAcYQ8gKLNnz9ZDDz0kSSotLdWoUaNUUlLSwVUBCAY31QEQlJ07d+qCCy7QE088oZkzZ+qOO+7QSSed1NFlAQhCVP8QDoDo0aNHD40dO1aXXHKJ5s+fT9ADMYRhfABBKSsr0+rVq5WcnKxevXp1dDkAQsAwPoCAKisrdfnll2vy5MkqLy/XO++8o8cee6yjywIQJPbsAfhVW1uryZMna/z48Ro1apQuuugilZaW6oMPPujo0gAEiT17AAAsx549AACWI+wBALAcYQ8AgOUIewAALEfYAwBgOcIeAADLEfYAAFiOsAcAwHL/Dxk1yT3WwFpCAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 576x396 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
@@ -1266,9 +2189,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 31,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The intercept alpha: \n",
+      " [1.92115887]\n",
+      "Coefficient beta : \n",
+      " [[5.06920615]]\n",
+      "Mean squared error: 0.26\n",
+      "Variance score: 0.89\n",
+      "Mean squared log error: 0.01\n",
+      "Mean absolute error: 0.39\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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iTsxnn+Ly+8d4XA82mc3bFHgoe99bunRpwG5PCPl9GMiJiCiiSO4JLyoFYmKCfi85974rhVPrREQUEaT2hLs6XNE4Cg8hiAPy7n2/VMtMeKxZE/J7WYQQIqzWKKSkpErrJkQ0uz2Bfaww9rHy2Mfq0Hs/WxznkPSL//C4rocjRqW0zIR3CzEcc2qdiIgMS2oaveLvX6C+T18NWhMYOYvBAJxaJyIiA0qYkiW9Fn7WoesgDshbDAbgiJyIiAzGWwA3CjmLwQAckRMRkUFIJbM5J04yVBAH5C0GAzCQExGRxvzVMrd+vcPrKPz8iwvUaqZspDLhsXp1yO/HqXUiItKMv1rmUgG89PBxiLaJKrZSfi3rxIdTEIYjciIi0oy3DG5b1sNeR+FGD+JyYyAnIooAShy1qYZAM7gDOeDErBjIiYgMrml62nqgEBaXyz09bYRg7q9m+bk/vs8A7gcDORGRwSl11GYwQp0R8JXBXXLWgbq7R8rVxIjFQE5EZHBKHbUZqEBnBKSCvVSpUsc7f+QoPAgM5EREBudtelqOozYDEciMgLdgfynXVVej5KwDtRmex4+SdwzkREQG5216Wq2jNgOZEfBXX7zkrAPlu/bK2i6zYCAnIjI4pY7aDFQgMwLegr2ItnIaPUwsCENEFAFaFhhRk3P6TMm17qYZAan94E1c3dWZ/o9kHJETkakZdf91oNT4fF5nBO5J9xnEAfWm/yOZRYgQTzJXmJ4PsY8EdnsC+1hh7GPlhdvHXrOmVZyWVlKony82Nwdxi19F9OGDjSd1PfcsSm4bHtS9pQJ43cA0RJWVut/XOW1GRPSzHFiilYgoBHrYf92SnCPoUD6fVHY5MjMDbsdlr7/qtbTquY/WoyIvH6XF5ajIy2cQlwnXyInItLTef92SvwNEghXK5/MV/P21QTKAF5UCMTE+X0fh4YiciExL6/3XLck9QxDK5wsl+EudEw40jsIZxJXHQE5EpqX1/uuW5J4hCOXzBRP8o378wWsA55Yy9TCQE5Fpab3/uiW5ZwhC+Xx1twyQvt7/lmY/25NtaH/T9c2uVeasZwDXgCpr5D/88AM2btyI2NhY7Ny5E1OnTkVqaqoatyYi8knL/dct+duPHYpgP1/M9n9KX8/fjvPwviecAVw7igdyl8uF7OxsvPPOO4iKisKoUaNgtTLHjoiopdr00XCgcU1cqy1avqb3va6Dk6YUj6h79+6FEALvv/8+ampq0LZtW9x7771K35aIyJC0niFwdevRuOWsBUt9fbOf63/ZDRXbd6nVrJC13BPvnD5TNzMwclG8IMzf/vY3zJs3D1u3bkVCQgKefPJJ9O/fHxkZGUreloiIQrFmDZCZ6fs5+qwj5snbZ1m9Ghg7Vv32KETxEXmbNm1w7bXXIiGhsWrNDTfcgG+++cZvIGdFLGWx6pjy2MfKYx8r4LbhiF22SnKtvvTQMYjEdoBB+jzxhRclg1z9/N+jIshKdUrTdWW3Pn36oLKyEi6XCwBQXFyMa665RunbEpFMIr0WOTVnT7ZJBvGSs47GIG4geiv4oxTFR+Rt27bFk08+iZdeegmJiYkoLy/HY489pvRtiUgGclcaI/2ylJUh6bouHtdLzjoaR4sGGYVfytt6v1YFf5SiSvr4kCFDMGTIEDVuRUQyCqdcJxmHVDb6+WeehXPG0xq0Rj5KbOfTI+4DIyKvzDI1aVZJ/2GHpbbW43qkbCnTw3Y+NTCQE5FXZpmaNCOz7AnXejufGliilYi80lstcgqfzwNOQsBkSO0xkBORV3qrRU6hi9mwTvYDTqTOLrdlPcxgrjJOrRORT2aYmox0UgG8fNs3cHUPb4mEyZD6wEBORBShlD7ghMmQ+sCpdSKiSFNbq8o54XIfu0qhYSAnIoog9mQb7P9hb3atZlSGIhnpTIbUB06tExFFgLa3DkCrfXs8riu5pcws+7T1joGciMjgJKfRi8sBq/L/xDMZUnsM5EREBqV0MhsZA9fIiYh0rmXRlcteyVYlmY2MgYGciEhD/iqjSRVdiX/lpWbPOfe/axnATYxT60REKorNzUHc4lcRffggGq7oiOiTRe7HpI6J9VZ0pQkDOHFETkSkkpaj60uD+KXiXl/k/v/eiqsIq5VBnABwRE5EpBp/o+smTcHbWzIbwKIr9DOOyImIVBJo6VLRpo3PIA6w6Ar9jIGciChEwR7hGegoOurcuWY/OxYv5Ql05BWn1omIQtC03t1EKlGtJef0mc1e08R1ZWev6+VN6+C19z8YfqMpInFETkQUAl9HeHojdb571fxsySCuxJ7wYGcQyBgsQgihdSOklJRUad2EiGa3J7CPFcY+Vp6WfZzUMREWl8vjurBaUVpcHtB7SK2DVz/7PC48Lv/6d8sZhCaBTNPzb1l5dntCyK/l1DoRUQhc3XrAeqBQ8ro/WpRW9TWDwPV2Y+PUOhFRCEI9wlOr0qreMuYDzaQn/WIgJyJTkWudWGq929c0tT3Z5jWIq8HbTAH3oxsfp9aJyDRCyTT3JZAjPOOyX0SbRQs9rqtdlc1bxjz3oxsfAzkRmYba68RSI/Dy/H/B9Ytfyn4vf2rTR8OBxs8affggXN16wDltBtfHIwADORGZhlrrxHo9JzyQGQQyHq6RE5Fp+FsnDnf93FJZwXPCSXUM5ESkW3IXMPGVaS517rct6+GA72lPtiGp29XNrtUNTGMAJ8Vxap2IdEnuxDTA9zpx4qB+kq/xt36u12l0Mg9WdjMpVmpSHvs4PImD+kkWXKlP6YWKvHwA8vZxKJXaJKfRz5wDLBZZ2qQX/FtWXjiV3Ti1TkS6pHYBk2D2WfvcEx5hQZz0j4GciHRJ7QIm3tbPLecq3Wv0bZ6azmQ20h0GciLSpVBLoIaqZaU215WdAQDRJ4vcyW9x761q9ppz761mACfNMdmNiHRJiwIml+6zbtc3xedzGcBJLxjIiUi3tCxgEiVxRjgACAClDOKkI5xaJyJqwZ5sg6+UNbn3txOFgyNyIqJ/a9/tKkRVVvp8jmjXTvb97UTh4IiciAiNo3B/QRwAROs4yetxry+Su0lEAWEgJyJT87UnXOq88agzpyTfR6n97UT+cGqdiEzJuuMrJN4z1OP6pdnoUsl2cYtflaw4p9T+diJ/OCInItOxJ9s8gvj5GU8FtKVM7f3tRP5wRE5EpiHHASda7G8n8oWBnIhMwWtt9BBoub+dqCUGciKKaDxmlCId18iJKCJdPmYkDzghU1BlRH7vvfciNjYWABAVFYX33ntPjdsSkUlJBfCynXvQcPU16jeGSGGqBPKBAwdi6tSpatyKiEyM0+hkRqoE8sOHD2P58uWora1F7969kZaWpsZticgkoop+Qvtf9fS4zgBOZqBKIH/00UeRmpoKl8uFcePGoU2bNvj1r3+txq2JKMJJjcLr+t2Cc3/dpEFriNRnEUIINW/4hz/8Aa1bt8aUKVPUvC0RRRqLl/PJ1P0njUhzio/Iv//+e3z77bcYM2YMAOD48eMYMmSI39eVlFQp3TRTs9sT2McKi9Q+js3NQdziV38uhjJ9pup7qn3uCdewz/XQN0qI1L9lPbHbE0J+reKBPD4+Hnl5eTh79iyqq6txxRVXYMSIEUrflogUEJubo+kRnt6S2eqv64mkjomaBk+t+4bMS/Wp9UDx25+y+A1beZHYx4mD+kkeGFKf0gsVefmK3Tdu4Uto84dszwdWrwYyMz0uO5atUj14atU3aojEv2W90fWInIgih7ejOpU8wlNqFF750QZcHDgI9ttukXxN3OuLVA/kWvQNEcBATkRBcHXrodoRngHtCd+/X/I5WgRPNfuG6FIs0UpEkmJzc5A4qB+SOiYicVA/xObmqHOEp9MZeGnVlBTJt9AiePJ4U9IKAzkReWhK3LIeKITF5XInbgGN68/1Kb0grFbUp/QKaD1a6kuBFHuyDfZrrvC47rWwy+zZkpe1CJ616aND6huicDHZzaSYvKI8I/exnIlbLbO5m1wa5EItrWq3J8Cx/F3ZzwY38jYyf20P5bMZ+W/ZKMJJdmMgNyn+h6k8I/dxUsdEWFwuj+vCakVpcXlQ7+XvS4HkNHpRKRAT4/e9w+ljbwEtkC8eeuWv7aF+NiP/LRtFOIGcU+tE5MHbGnMoa8/eEs+s+/d5L+wSQBAPh7elg6bgLiXu9UWKtsmXQJcm/LVdj5+NwsdATkQe5EzcCjT4q3lOuK+AprdtZL6+dLTkr+16+2wkDwZyIvIgZ+KWty8FTapeWqj6KWW+ApqcsxFyCGYU7a/tevtsJA8GciKSVJs+GhV5+SgtLkdFXn7I68O+Xldy1oGaR/471CaGzFdA09s2smBG0f7arrfPRvJgICci5QghuQ7uWLZK07PCfQU0vW0jC2YU7a/tevtsJA9mrZsUs1CVZ/Y+9ralrIkcASTsrHWZt60pQQ9Z9Gb/W1YDa60TkW6069MD0aeK/T5Pi3rol6pNH63LwN1SbfpoOABDfOkgbTCQE5FspEbhIjpack86M6UDZ5QvHaQNrpETUdjsyTave8KZKU2kLAZyIgpZ9P5CvwecMFOaSFkM5EQUEnuyDe3S+jW7Vnv3KI9sdGZKEymLa+REFJRQDjjhGi+RcjgiJ1JZoHWz9Sjgc8KJSDUckROpqOWe4Ka62Q74roCmtVCPGSUi5XFETqQipU6fUmqUnzAli6NwIp3jiJxIRUqcPqXUKF8qgFds3or6X90Y8nsSkfw4IidSkRJ7quUe5fvaE84gTqQ/DOREKlJiT7Vco3xLWRmn0YkMiIGcSEVK7KmWY5RvT7Yh6bouza4Jq5UBnMgAuEZOpLJw9lTH5uYgbvGrPx+eMX0mnNNnSp6OFcgon9noRMbHETmRQTQltVkPFMLicrmT2gCENMqXnEY/XckgTmQwHJETGYSvpLaKvPyAR/kchRNFFo7IiQwi3KS21qtWMJmNKAJxRE5kEK5uPWA9UCh53R+pAO5Y8jZqx46TpW1EpB0GciKDCCWpjdPoRJGPgZzIIGrTR8OBxjVxd9b6tBnSa+P19bB3audxmQGcKPIwkBMZSCBb1zgKJzIXBnKiCMEATmROfrPWt2zZokY7iMgPXyecSQXx0h9OMogTmYDfQL527VpkZWWhuLhYjfYQkQRvxWB8HXAi4hM0aCkRqc1vIF+xYgVGjhyJCRMmYPny5aivr1ejXUR0CW/FYFrinnAi8wmoIMxdd92Fjz/+GGfPnkVGRgZ27dqldLuI6BL+ir44p0xnACcyqYCS3Q4fPozvvvsO1dXVOHPmDCZOnIjhw4dj9uzZuOyyy5RuI5HpeSsGAzCZjcjs/I7Ib7zxRkybNg179+7FzTffjJycHOzatQvXXnstHn/8cTXaSGR63oK4Y9kqlVtCRHrjd0S+ZcsWtGvnWVjioYcewtq1axVpFBE18ralrD6ll/diMERkKn4DuVQQb7J06VJZG0NEjS5PH46Y7ds8rnManYhaCuv0s2uvvVaudhDRv9mTbR5BvHzHtx5B3Ne+crXpqS1EZsPKbkQ6EUxltqZ95U2a9pU7ANWn2/XUFiIz4nnkRBqLOnE86HPCve0rj3t9kaxtC4Se2kJkRhyRE2lIKoDX/7IbKrb7rtXgbV+5v/3mStBTW4jMiIGcSAPhHnDibV+5q1uPsNoVCj21hciMVJtar6mpwd13340FCxaodUsiXQp2Gl2Kc/pM6evTZoTcrlDpqS1EZqRaIF+8eDFSUlLUuh2RLOTMxvZ1wEmwatNHw7FsFepTekFYrahP6QXHslWaJJfpqS1EZmQRQgilb7Ju3TrExcXh0KFDcDqdmDVrlt/XlJRUKd0sU4nNzUHc4lcRffhg41Toc8+i5LbhWjdL11pmYzcJNEjZ7QkoKalCm/nzEPfGax6Pc094+Jr6mJTFflae3R76aYWKB/KjR49i/fr1mDFjBt54442AAznJaM0aIDPT8/rq1cDYseq3xyhSU4G9e6WvFxQE9h4Wi+e1jRuBu+4Kr21ERP+meCB/++234XK5EBMTg/z8fFy8eBFDhgzBhAkTfL6O3/7kkzion2QyUn1KL1Tk5WvQImNI6pgIi8vlcV1YrSgtLvf52nCT2YLVcsbFOX2mKaa2OVJUByNptfUAABbHSURBVPtZeeGMyBXPWp80aZL7/9fW1sLpdPoN4iQvbg9qLtCgF1I2ttMJ+zVXeFxWchqdBVmIzE21ZLdPP/0UO3fuxO7du/HJJ5+odVuC98Bjxu1BTUHPeqAQFpfLHfSkktiCzca2J9tUD+IAC7IQmZ0qyW6h4DSOfMJN2ookwS4zxObmIO71RT+P3iVOHPM2jQ4hVPk7DmcJwOg45asO9rPywplaZ4lWE5DaHoTVq00XxIEQlxmE+Pl/LUhuJysqVTUjnTMuRObGym4mUZs+ulngttsTABN+ww5m3dvX2rPUDAegzZYy5/SZku1hQRYic+CInEwlmHVvb2vPUkEz2MpscmJBFiJz44icTKU2fTQcgN91byCwrP7qeS/iwmOPK9DS4LSccSEi82AgJ9MJNOh5m4ZvwspsRKQHnFonkhCbmwPLuXOSjzmWrWIQJyLdYCAnQ5LzMBOp97ZlPYzo4iKPx7j2TER6w0BOhhNMUZdgtU/p6jUjvT6ll2QQV/JLBRGRPwzkZDhKVTKzJ9sQVVri9XGp5Dclv1QQEQWCgZwMR+7a8d7OCW9Jaq85y6MSkdYYyMlw5KpkFl24TzKAO5atkny+1F5zr18q9u8DUlM5MicixTGQk+EEe5iJFHuyDe0G9292re7W21Fy1hFUgRVvXx4sALB3L6fZiUhxPDTFpIx+CEIgh5lIkfuccG8H0lyK574rx+h/x0bBflaers8jJ1JCKJXMJA84CXM/eLNKcfv3NY7EWzDrue9EpA5OrZOuKLGVy1sym1xFXWrTR6MiLx+u63pKPs5TyIhISQzkpBtyb+WKnzbZawBXojKbHGv3RETBMlUgZ+EOfZNzK5c92YbLVv//ZtcqPv1c0dKqLZPkkJrKSnBEpDjTJLt5S0oy6z+09n9sRP0LL/6cLDZ9pub9kNQxERaXy+O6sFpRWlwe0HvIncwWDiYIKY99rA72s/LCSXYzzYichTt+FpubA2Rm6q4aWTj7wy0V5apOoxMR6YVpArnc1cCMTK9fagJdY265RGJPtiGp+zXNntPQJj7oAM6lFyIyItMEcrmqgUUCvX6pCaQQi1RCXEslZx0o+7E4qHubpWY6v6wQRR7T7CN3Tp8puUZuxoxiV7cekgFQD19q/O0P9zabAAAlpyuBqNC+m/qapdA6d0AuLfNEmr6sOICI+YxEZmSaEXnL0Z7rys5wXdkZCZMfNd3IxMjbpKS+gACNCXGhBnFAv7MUctLrkgoRhcc0gRz4uXBH1ZvLEX2yCNEniyJ6GtWb2vTRwOrVAdUS14vWf/qjzxPKwp1NMMPSixm+rBCZkWmm1i9lhmlUv8aORcVtw7VuRUACOWI03NkEMyy96HlJhYhCZ6oReROOTIzBW2nVQE8mC0YwJ54ZlZGXVIjIO1OOyDky0bn6etg7tfO8fF1Pd+EaJQKsUu8bqNjcHMQtflWxIj3NDngJ8tQ4ItIvUwZyM0yjGpWvafRIzrJWK6Nc6y8rRCQ/U06tm2EaVSlK7UNu3+OagNbCgcjMsmZGORGFypQjcoAjk1AoNWqUCuClP5xE+19eJVl7PRJzGZi3QUShMuWInEIj96jR1znhIj7BFFvCmpjpsxKRvBjIKWByjRqt/9oZ0AEnZsqyNtNnJSJ5MZBTwOQYNdqTbUgcdluzazXDRkgecGKmXAYzfVYikhcDeYjMePhEOKNGb9PoANB60yde+6+pGl9pcTkq8vJ9BjZfvxMj/L6C+axERE1Mm+wWDj0ePqH0HmQg9H3IgWSjh1tVz9fvBIDufl9ERHKxCCGE1o2QUlJSpXUTvEoc1E+yoEx9Si9U5OWr3p6WQayJr6lZuz1B8T4OdDsZ0HjoSWlxecj38vU7gRCa/L7U6GOzYx+rg/2sPLs9IeTXcmo9BHrbKqS3PcgJj030msxWf11PydeEm53t63eit98XEZGcGMhDoLetQnoKVPZkG1qvXdPsWtnOPe5kNqWys339TvT2+yIikhMDeQj0tlVID4HK157whquvcf+sVHa2r9+J3n5fRERyYrJbCPR2+ISWteOjzpxG+97dPK5LbSdrokRVPX+/Ez39voiI5MRktwgRm5sTVKCSI3lFagRee/sdcPyv/rZ2aYEJQspjH6uD/ay8cJLdOCKPEGrWjveWje5rFA6os0WOiMhsGMgpKJLr4GfOARaLz9d52+ft3Pk1zr/0iuztJCIyCya7UUB8JbP5C+KAjy1yK5fpssoaEZFRMJCbQDjlSVu/uzKgA0788bUVjmduExGFjoE8wjVNaVsPFMLicrmntLFmjd/X2pNtSJjVPPO9YsOWoAJ4E19b4VoGeSPURSci0gvFA3lDQwMmTpyIpUuX4rXXXsOMGTNQU1Oj9G1VYYSA421KGy+/7PU1vqbR62+6OaR2eNvLDTQP8t6+eOixb4mI9ECVEfn111+PKVOm4IknnsCFCxewZcsWNW6rKKMEHK9T2vv3e16rqZFlGl1KbfpoOB/Jknzs0v3ueis3S0Skd4oH8qioKEyePBkAUF9fjzNnzqBLly5K31ZxRgk4Xqe0U1Ka/WhPtsF+VXKzaw3t24cdwC91/qVX/FZ101O5WSIiI1CtIMy2bdvwpz/9CX369MHjjz+uxi2VZbUCLpf09YsX1W+PN2vWAJmZntdXrwbGjgXatAGcTs/HtaoTlJoK7N0rfb2gQP32BGPNGuCllxpnO1JSgNmzG/uYiEhBqld2e/rpp9GnTx+MGzfO5/P0XkUo0KNM9VAERarqm23iQ5LbxkqKSoGYGFXbd6lQjmTVA6O22+hYcUwd7Gfl6foY06NHjyIvL8/9c+fOnVFUVKT0bRUXyEEcellHr00fjYq8fJQWl6MiL78x4EgF8bMORYJ4MEmBSh2qojQ9LbUYIQmTiOSj+Ij8xIkTWLhwIVJSUlBfX4/vv/8ezz77LOx2u8/XGeHbn7/65oGO2tXSakc+2t5zp8d1OdfBWzLLSDWpYyIsEkstwmpFaXG5au0wS3834UhRHexn5YUzIuehKQpS4x/3QKfupbLRHW8uR+0YZddw9fZlRil6+Zx6aYdaGGDUwX5Wnq6n1s1M6XPCA5m697YnHEIoHsQB82Sh6+XMc7P0NxH9jIFcQUr/4+5zXVYIxfaEB7MGq/SXGb2QWtvH6tWqT2ebpb+J6GcM5ApSOnHL2yjLun8f7B0ub3atwXa5LGvhwSbw6WWkqoaWSYVabD0zU38TUSNTBnI1s3pb/uMu5wgt0FFWyVkHyo7+JMs9g83ONmoWulGxv4nMx3TJbpGU1evtszQp/b4IIkFifRyhJ6/oJTvbCJggpDz2sTrYz8pjslsQ9LTfN1y+vniUnHV4DeLh4BosEZG+mC6QR0pWr6WkRLFkNl+4BktEpC+mC+SRMKK0J9uQ1LNrs2uOt1cqGsCbcA2WiEhfTBfI1R5RyplY1+43fbyOwmv/695wmhk8IX7+HxERacaqdQPUVps+Gg7AZ2lVubRMRmvaquWA7/VtKd4CuNrk/ExERBQ+02Wtq0mOcplSAbzh8rYoO3IirLaFmoVqthKg4WCmr/LYx+pgPyuPWes6FU5iXcymjV5H4eEG8XBESrIgEVGkMN3Uuppc3XpIjl79JdZJBfCy3QfQ0OlK2doWqlA/ExERKYMjcgUFm1jn7YCTkrMOXQRxgNvPiIj0hoFcQQFv1bpwQZM94aHg9jMiIn1hspvGpAL4+adnw/nkM8rel8krimMfK499rA72s/LCSXbjGrlGbA89gNiN6z2u620ETkRE+sZArgHJafQz5wCLRYPWEBGRkXGNXEW+ktmUDuItK8xhzRpF70dEROpgIFdB9N49miazNVVjsx4ohMXlatw+lpmp6DnsRESkDgZyhdmTbWh324Bm18r/8U9V18Ij6ehWIiJqjmvkCknqmAiLy+VxXYtkNlZjIyKKXByRy62hAfZkm0cQ13JPeCQc3UpERNIYyGVkT7bBfkXbZtcuPPyo5lvKWI2NiChyMZDLoPUfl3lNZqvOll6fVpNUNTasXs1qbEREEYBr5GGSDOBFpUBMjAat8a42fXSzwG23JwCs1EREZHgM5CGSCuAAK7MREZG6GMiDZCkrQ9J1XTyuM4ATEZEWGMiDIDUKr/jHNtT37qNBa4iIiJjsFpCEqf/tNZlNz0G8ZVlWVnIjIoo8HJH74bU2us41lWVtYj1QCFvWw3AAzFYnIoogHJF7IXXAiXPS1ICDuNajYZZlJSIyB47IW7D+aycSh93mcT2YUbgeRsMsy0pEZA4ckV/CnmzzCOKlP5wMeipdD6NhlmUlIjIHBnIA7fqmeF0LF/EJQb+fHkbDLMtKRGQO5g7kNTWwJ9sQfbKo2eVwDzjRw2hYqiyrY9kqJroREUUY066RS43AK/+6CRf73RL2ezunz2y2Ru6+rvJouGVZViIiijymG5HHrM/1Oo0uRxAHOBomIiL1mGpELhnAz5wDLBbZ78XRMBERqcEUI/K2QwZ5BPHq5+Y3roMrEMSJiIjUEtEjcktlBZK6Xe1x3QiV2YiIiAIRsYFcahq99IeTIW0nIyIi0ivdT60HW+o06vgxjyB+fsbTIe8JJyIi0jNdj8iDKnUqBGwPPYDYv21odpnT6EREFMl0PSIPtNRpq62fwd7h8mZBvGzPIQZxIiKKeLoekfsrdWqpcqB9jy6wXLzofqzyw1xcHOx56AkREVEkUnxEfuLECcyYMQMrV67Eiy++iKVLlwb8Wl+lTuOy5yOpa2d3EK8dMRIlZ84ZPohrffwpEREZi+Ij8srKStx11124/fbbAQB33XUX0tLS0KtXL7+v9Vbq1Lp/H6z797l/LvtuPxqu7Cz5HrG5OYhb/CqiDx+Eq1sPOKfP1G2hFj0cf0pERMaieCBPTU1t9nNDQwMuu+yygF5bmz4aDjSuiUcfPghYrbDU1Lgfr3plMWr+n2egb2K0wOgrJ0CP7SUiIu2pmuz297//HQMGDEDXrl0Dfk1t+mg4J02Bpb7eHcTrf/FLlBSV+gzigD7OBQ+GHo4/JSIiY7EIIYQaN9qxYwc+++wzzJ49G1FRAX5/OH0a6Nix+bVdu4Abbgjs9VYr4HJJX78kQU43UlOBvXulrxcUqN8eIiLSPVUCeV5eHnbt2oWZM2fi7NmzKC4uRt++fb2/QAjU3Hc/Wq9d477kzHoM5+e/HNR9Ewf1g/VAocd1V+fOEAmX627dvOVSQBMlTk6z2xNQUlIl63tSc+xj5bGP1cF+Vp7dHnrBMsUD+b59+zB+/Hh3cpvT6cS4ceOQkZHho1XNDzIpPXwcom1i0Pf2Fhil6OWY0djcHHdOgKtbDzinzVCkXfwPU3nsY+Wxj9XBflaergN5SP4dyM+9txp1w4aH9VYtA6OlshLRxUUez6tP6YWKvPyw7mUk/A9Teexj5bGP1cF+Vl44gVyfBWFmzULJE78DAl1L96HlueBJHaVH9kwoIyIiI9Jnidbs7JCDuL+CKr6KzBARERmNPgN5iJrWxK0HCmFxudz7xi8N5s7pMyVf65w2Q61mEhERySaiAnkg+8Zr00fDsWwV6lN6QVitqE/ppZtENyIiomDpc408RIEWVGm5bk5ERGRUETUi5/o3ERGZTUQFcq5/ExGR2URUIOf6NxERmU1ErZEDXP8mIiJziagRORERkdkwkBMRERkYAzkREZGBMZATEREZGAM5ERGRgTGQX8LfgStERER6E3Hbz0LVdOBKk6YDVxwAt7MREZFucUT+b4EcuEJERKQ3DOT/FuiBK0RERHrCQP5vPHCFiIiMKOICeagJazxwhYiIjCiikt3CSVirTR8NBxrXxKMPH4SrWw84p81gohsREelaRAVyXwlrgQRkHrhCRERGE1FT60xYIyIis7EIIYTWjZBNaiqwd6/09YIC9dtDRESksMgK5ERERCYTUVPrREREZsNATkREZGAM5ERERAbGQE5ERGRgDOREREQGpllBmPz8fGzZsgXt27eHxWLBlClTmj1eW1uLBQsWoEOHDjh27BgmTpyILl26aNRa4/LXz8uXL0dpaSmSkpJQWFiIxx9/HF27dtWotcbkr4+brF+/Hk899RS+/fZbtGnTRuVWGpu/PhZC4P333wcAnDx5Eg6HAy+//LIWTTUsf338008/YeHChejduzcOHDiAESNG4LbbbtOotcZUUlKCxYsX4+DBg/joo488Hg857gkNOJ1Ocfvtt4va2lohhBBTpkwR+fn5zZ6zbNkysXz5ciGEEAcPHhSZmZmqt9PoAunn1157TTQ0NAghhNi4caPIyspSvZ1GFkgfCyHE0aNHxaJFi0S3bt1EdXW12s00tED6ODc3V+Tm5rp/PnDggKptNLpA+vi5554T7777rhBCiMLCQjFkyBC1m2l4mzZtEv/4xz9Eenq65OOhxj1NptZ3796NTp06ISYmBgDwq1/9Cnl5ec2ek5eXh759+wIAunfvjoMHD6K6ulrtphpaIP08ffp0WCwWAEBDQwPi4uLUbqahBdLHFy5cwMqVK/HYY49p0ELjC6SPN2zYgMrKSvz5z3/GokWLOOMRpED6OCkpCeXl5QCA8vJy9OzZU+1mGt6dd97p828z1LinSSAvKytr9mHi4+NRVlYW9HPIt2D6sK6uDrm5uZg+fbpazYsIgfTxa6+9hsmTJ7v/kaTgBNLHxcXFqK6uxoMPPoj09HQ88sgjcLlcajfVsALp44ceeggFBQV4+eWX8eabbyIjI0PtZka8UOOeJmvk7du3x/nz590/V1dXo3379kE/h3wLtA/r6urw/PPP44knnsBVV12lZhMNz18fnzp1Cg6HA5s2bXJfe/fddzFo0CD07t1b1bYaVSB/x/Hx8ejTpw8AoEuXLqiursapU6fQuXNnVdtqVIH08TPPPIMxY8ZgxIgRKC8vxx133IHPPvsMbdu2Vbu5ESvUuKfJiPz6669HcXEx6urqAADffvst0tLSUFlZ6Z5GSEtLw3fffQcAOHToEHr06IH4+HgtmmtYgfRzTU0N5s2bh4ceegi9evXCp59+qmWTDcdfH3fs2BHZ2dmYOHEiJk6cCKBxZMMgHrhA/o779euHn376CUDjP34ulwt2u12zNhtNIH186tQpd5/abDZERUWhoaFBszZHCjninma11rdv345PP/0UiYmJaNWqFaZMmYKFCxeibdu2mDhxImpqarBgwQLY7XacOHECWVlZzFoPgb9+njJlCo4cOYLk5GQAgNPplMymJO/89THQuKa4Zs0avP7665g8eTLGjh2LDh06aNxy4/DXx1VVVXjllVfQqVMnnDhxAkOHDsWgQYO0brah+OvjXbt24c9//jNSUlJQVFSEnj17IjMzU+tmG8o333yDdevWYdu2bcjMzMTDDz+MJUuWhB33eGgKERGRgbEgDBERkYExkBMRERkYAzkREZGBMZATEREZGAM5ERGRgTGQExERGRgDORERkYExkBOZ3O9+9zssXboUAHDs2DEMHToUhYWFGreKiALFgjBEJnfmzBlkZGRgxYoVePLJJ/HCCy/gxhtv1LpZRBQgTQ5NISL96NChA0aNGoVx48ZhyZIlDOJEBsOpdSKTKysrw5dffonLLrsMHTt21Lo5RBQkTq0TmZjD4cCECROQlZWF8vJyfPHFF3jnnXe0bhYRBYEjciKTunDhArKyspCZmYmhQ4dizJgxOHbsGHbs2KF104goCByRExERGRhH5ERERAbGQE5ERGRgDOREREQGxkBORERkYAzkREREBsZATkREZGAM5ERERAbGQE5ERGRg/wfemR2en+GFBQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 576x396 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import numpy as np \n",
     "import matplotlib.pyplot as plt \n",
@@ -1640,7 +2588,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1688,7 +2636,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1749,7 +2697,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1791,7 +2739,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -1805,57 +2753,7 @@
       "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",
@@ -1885,7 +2783,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1907,7 +2805,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1925,7 +2823,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -1936,12 +2834,12 @@
       "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"
+      "  1.17385778e+00] 15.212327334149508\n"
      ]
     },
     {
      "data": {
-      "image/png": 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yPR6eHwz/K1Ky29lWCxsbLGKz4lgtDs1Ls8w7sY7amQlSiak9t1/CbfXq3/Ltb39rdCzP+eefz6pVtwOVVvjSpcdw0UWXcMopp01ySY8sJcMkKWzO0/pXraR3+3hx6LkozlkrF2kTCqkqy7JwZ8RwZ8RInjj2NeMbvN0eXkcZbzjES20l+rbmyLeXKOZ9Cn0eZmtlPYABcqwfHvzmzo5RtyhNYm6c5mMqA9/cFoX4dHIoLeEj7ZRTTmPlyv8XYww7dmyftHJUmwK7ynoGiqz+yQ6aflYk6PEoz7Y57e+Xkl2QUljLlGI5FrGWGLGWGCzfO11tFhAUA7z2MrkdBfq35cntKNCzZQh/l4eV9zHbyvRu6wOggy5iro1lQ3ZuivjcOM5Ml4YlGTILkriz4zgNjq6LyyGzLIsFCxa+5XnbrvwufeONnTQ0NJJOv3W6ZRgpsKtszb+34jw4SK+BPcdZzLtmFg1HZQ7+RpEpxE7YxBcmiC9MUH/W3nXojTEUd5fYvLaH3q05SjuLDO0oEusJSPRDT2uOYGtlb+GddFUWi7Equ765s2NYM12yi1LUH5UmOT9Bck4CO6k/ZKViw4Z1rF37EuVymV/96pecd977AHj88cfp6GjngQd+yrXXXsfpp5/Bf//3A1iWxZe/fOMkl/rI0W5dVWKMofs/u3j1zp0YA+1n2PSe4fDJi48jGZ9afzdNhfoKC9XVwZU9n4FcmaGBMp3bcnRt6INOn/zOAvEeQ7IXnML+3xtzbWIzXJyWGMm5CTILk8TmxGlckibZkpjWrXJ9b43fdKor7dY1yYwx9N27h877ugksyF2e5uJPLwaYcmEtcqTFXIfGWofG2iSnnDRn9BdrsezTN1SiVPbZ3ZZjaHse0+ExuL2A3+7h7gkwfQHl9hK0lxhYO0TXPsd1kzbBDIf4nDg1C5KkFiRpWJym7qg0Tkqrvcn0o7Sogv4He+j7WQ+DxTLb/tDm5AtnjO4+JRJViZjDzPoUAPOaa+CdY18PAkP77iH2tA6R31FisDVPub2Ms9vHdHowFMDOgMLOMoXnK93sO6hsdBPU2tizXDILkiTmJqhdlKJpaQ3plvjo9U2RsFFgT7D+X/Sy50fddPcXWH9mwMBim6Pn1R38jSIRZ9sWc2bWMGdmDZw+9rWyF9DbmWdoZ5Ge1weHW+VlvLYyTk+A1Rdg+koMbiwxCHQDWwETB2uGS2xuHHuWS3ZhipnHZmlYnCaW0q9Dmdqq+h16++2388Ybb9DQ0EBraytf//rXSSaT1SxCVQ39dpCee3azp7/AujM8Bo51OWf57Cm//7TIVBdzbZrnZGiek2HRuxpHnzfGUCz65NuLdG4coH9rnlJbiVJbCdPuYecMtHmU2yrbzxbpZzcdAFiNDrHZcdzZMVILEjQuyZBekKR2VhJHMzhkCqhaYHd1dbFq1SqeffZZbNvm2muv5ZFHHuGyyy6rVhGqatOL3bR9dTuN8Tib3unTe5zDR887erQLUESOPMuySCZdkotcGha9dfZFsbdE16ZB+oaDfKi1cmt3B5g9PqU9eUqv5skB3cNXzE3Cwpk1PPBtXoLsohTJ+QmajkqTqdW+8lI9VQvsVCpFLBZjcHCQ2tpacrkcS5curdbpJ4znB3TsyTFnRmZ0xGqQC+i+ZRemYNg8t0THOy2WzqtTWItMskR9nHmnNzLvzV3sRZ/OLYP0bh6i+EaJoe0Fym0lnN0BDAUErWWC1jLl53MMDG93usUCGmyY6RKbE68E+dwE9YvTNM1P4+hauRxhVZ3W9cADD/Df//3fNDc3Y4zhq1/9KpnMgecge56PO8X39v31izt4as0bXH7uEpYfXflcrV9v5Xf372Sg3rDlwy4mZvGZD5zInBk1k11cETlEfR152tf3M7A1R++WIfKtBYJOD7+zjPH3/+vTJCzKTRZ2S4zsojSxuXFqFqWYdWwd6ZoY2XRszO57IuNRtcBev349X/7yl7n//vtxXZdvfOMb2LbNl7/85QO+JwzzsH/8+Ou0dQ2xbGED73/XAgZ+2cfuOzvZ0jPAlv/H5eh3NpGKO7z7hFkTcv6JMJ3mNE401dWhmU715ZV89mzJMbSjQP/WHEPbC5gOD39XmSAX7P9NFhRrK5uoBM02fqODPculbnGaeJ1LKhmjLhPHdS3mzqrDlD1c18ZCW+q+nen0fTUl5mF3dHRQX1+P67rDhWpm165d1Tr9hDDG0N1XWfFhV3eO8q4SPffspljy2XGuTd3CNH9w8txJLqWITAQ37jBzWRaWjf0Fa4yh1OtR3FmkZ8sQ/dvy+LvKlNtKeF0e8Zwh1R8QtPqAD5SAHMUE9NdDa71FsR78mS6DGZ9SLcRTLk11SZIxh2TCpS4TJ510ScZcknGHZNwhm4mTiE3tHkk5PFUL7LPPPpsnnniCb3zjG2SzWTZt2sRXvvKVap1+QgzkyhRLla0I+wYKdHyvHVM25E506VtisahB16xFosayLBINMRINMWrfMfYymCkbyh1lym1FBrbl8ds9cjsKFHYUMYUAb9Dg9QWYbQCGQinAAMW6EsX6Mvl66K2HbQ2Vvcv91N5Wt21bLJ5Ty6LZtcxvrtFaD9NQ1QLbcRz+5m/+plqnq4quvvzo/aZ1hoF1Q2RbkrSfC+yGFg0yE5F9WDGL+Lw48XlxMu/a2zI3xhD0+ZTbypTbS3htZWJ90Pf6IF6Xh+8HlAYCTL/B32oo+wFBYPASUKiHfB30pHw6N/eyo6GXUhZqahPMa65h/swajpqd1aqK04D+Bw/D7t5Kd3isCLOfMxRiPtnLs7QNdQLQ0jg9dogRkYllWRZOvYtT75I8vvKH/sh12aBU2Rmt3FbGay9VbneVKLeVCIoGikAn+H7AUKFMvuiT93wKNXnyDQVert/Ni41WZQT7kjRLjm5gzoyMus9DSIF9GHYPt7CXv5bEKw6xs6XEkz07wbJIxB3qs5qjKSKHx47bxBckiC8Y+/vEGIPf6+O1lSgPXyOvGWmhd5UpeQH5bo98m0e+6AN5IM/mVDfr6sFuiVG7OE12YYr6xWnmHJPFVYhPaQrsw9DVWyDZbWjeYGh3LN54r0U2k2DezBqOW9iArVGdIjJBLMvCbXBxG1ySJ4x9bWS/8vKuEt6uMoUdRQZa8wxtL5AbKBNr9zG7ypTX9LGHPvYAW12wml3cOXHqj0pTP7yRSmpeAjujIJ8KFNjj1D9UYtPOPk5a2oRj2xRLPn2DRRY/Z0jEHJb88SyO/0g9M+qSmn4hIpNq3/3KAeqAFoZb5Xt8im8U2P3aEN2bBym9UaL0Romgx8fs8ijt8uh8MUfn8LHiMRun3sWdFSO7KEXtohQ1C1Mk5yVwZrhYtn7fVYsCe5x+/ptWOnty7BkocMFp89nRNUi63dC8y8aZazP7ypk4tforVESmLsuycJtc3KYaMstrWEjL6GsDPZVpaN2bh+jZNES5vYTp8DG9AVZXCbpKDLw8RBuVHdHiMYdYwsaZFSM9P0lqfoLM/CQ1i1LEZsexU1rp7UhTYI9TZ08OgPXberjgtPm0tg8w+4WAVCJB9v31CmsRCbVsQ4LsqQkWnLp3M5Wy59PRk2dgZ4G+LUP0bs3j7SpBp0+i1yc24MNAmb5NudH3uI5FzLWx610Sc+Mk5yVIzk/QuDizt1WuXsjfiwJ7nNLJGLlCGWMMxhjaX+pl7k7IzI+Rvbh+sosnInLExVynsld5cw2cPGP0+WLZp7MnT/+eAv1b8/RtzRG0e/gdZWJ7AhK9PlaHz1BHEVZXViDbQiXM7YRNbFaMxNwEqfkJahalqDsqTXJuAjupVvnbUWCPUzLukCuUAdi0s4+a35RxHIumixtxatS6FpHoSMQc5s+sgZk1sGzv88YYegaK9A+W6H8jT8/mHOW2EuVdJYJdHoleg5vzKW3xGdpSGHNM17FwGl2c2THis+PUHpWifnGG7KIUbpNa5aDAHrdi2R+9/+JTbczeZkhlYtS+X61rERGoXCNvrE3SWJuEObWwz65onh+QK3j0dhXYvWmQoe0Fim1F/F1l6PQxfQavqwxdZXK/y9FLL9upXC93kjY0V3ZFSy9IkF1YGfjWsDiNk4xOg0mBPU4jS5ACxH9TBAO1K+pw6lWFIiIH4zo2tZk4tZk4CxbVjnktCAx9A0V2bxlicHue3I4iue0F/PYybneAyQewvYS3vUT+2UG6h99n2xZ2k0NqfhKaK7fZRUmaj64h0Ryfdq1ypc04eH6A51d237FLhobXDI21CRZ9JDw7cImITFW2bdFQl6Th5CScPPa1YtmnryNPX2uewW0FBrfn8dpKBB0eZk9A0OUx1D1EEBgG6KMT2Aw4qcpe5cl5cbKLKgvENCwZvlYeD+e1cgX2OBSGW9fppMtpQzXEMzlmnFJLfJ4W1xcRmUiJmMPMeTXMnFcDZ459bWCgxJ5tQ5jdAZ3r+yi+UVmy1XR4kA+gtcRQa4mh/x2kffg9rmsTa46Rmp9gxtIaskeliM2KE5sTw65zpnSrXIE9DiPd4YmYw6yNFqV0jJrzaw/yLhERmUjZbJzsO+Jv2Q/b8wN62/P0bcvTPXy9vLyrDJ0e9AZ4u4rkdxXpfaGfWU2Z4dk/kKmPEZsTx51dCfDY7DjunDixlhhWbPKDXIE9DoXhAWe13RalHSWcrEP69JqDvEtERCaD69jMmJthxtwMS87cOx3NDwJ27ynQsamfna/207d5iO6+HIkeQ6IXaoZcGvrLJDYXKqPdhlkWuDNjuLMrIR6bHauE+tx4VdfgUGCPw0gLu25jADhkzsxiuZP/15aIiIyfY9u0zEjTMiPNCWfM5MGnt7G9Y4BE3AFj8Pp9kj0FWrw4R7lpUr0QdHoEXR7xXZW9zPNr9i4SY1nQePVMas6pTo+rAnscCiUPyzdkNvgQj5F+r1rXIiJh5tg2l753EVt39VfmlAOrX+vi1a172FIss4U+mAMcD5ZvSA5YLCDFXJOkqeRid/r4vX5VF3tRYI9DseST3QluHmKL4sSP0raZIiJhF3Ntjpm/dy2NM98xmzOOb2HTzj427eitXA41EBhDp5tno8mzkTyWZZE5yiUIDH8wp4Gjq1ReBfY4FMo+9ZsMtm2RObNmSo8iFBGR35/r2By3sIHjFjaMeT5X8Ni6q5/Nb/SxvXOQwVwZy7Ioef4BjjQBZavamUKsmPeobTU4KUi/OzvZxRERkSpLJ11OOKqRE45qpOz55Is+ybhDPKZBZ1OKv7GIUwJ7SWV4v4iIRFfMdYi51V8SNZzLvVSZs64EQOyk1CSXREREoiqygd3Zm+c3r7SPLjk6wvMDunrzGGMAMIEhtr6yS1fqtEzVyykiIgIRDuzfbujk+fUd7OgcHPP8M6+085+PvsbOriEASluLMBhQykJ6cXIyiioiIhLdwPa8Ssu6VB47wq9/qNL93d1f2au18LscgTEMLLBIxHXJX0REJkdkAzsY7vL2fDPm+ZEu8nzRq9z+rrILTP98i2Q8OvuuiojI1BLhwK7cvvUaduWFfNEjGPIpbCpggMJ8G9eJbHWJiMgki2wCmXG0sAvr8vi+YWgWxLLqDhcRkckT2RQazmv8YGwL2x8O8FzRo7A+h+8HDCy0qElp/rWIiEwetbDf1CVeHmlhFzwKGwp4vmFgrkU2rcAWEZHJE+HArtz6b+oS94cDu9hXptxWwrMM+WbIpuPVLqKIiMioyAZ2cIAWtjc8Gs1t9THGUJhlYRy1sEVEZHJFNrBHWtgjAT1iZH52zS5DEBgGZ1V25lILW0REJlOEA7sS1Pt2iRtjRlvcmXaDHxh6myuvq4UtIiKTKcKBXbndt0vcH25tW2VDajf4xrC7obKASq1a2CIiMokiG9j7u4Y9cj/VDZYP5WabvBXgOrZWORMRkUkV2cAe7RLf5xr2yCIq6c7K7UDj3u5wy7KqXEIREZG9IhvY+1uadOR+uqvyeHe2sjFIjbrDRURkkkU2sPc36Gy0S7yr8lxHurJzV60GnImIyCSLcGBXbscMOvMNdtmQ7AVjQ76p8rymdImIyGSLbGCPDjoLxrawU7vBAgqNYNzKdeum2uRkFFFERGRUZAN7b5f4vtewDelOg+vY5GdUwvqdS2ewZG7tpJRRRERkROR369pFbe/FAAAdu0lEQVR3e00vqLSwY67NCefM4D3n1TG7KTNJJRQREdkrsoG933nYXkByj8GyYNEpDSSaUpNVPBERkTEi3CVeuQ0Cs3erTS8g2QuWZRGbq4FmIiIydUQ4sN+6YIrfVcbygFobO6OVzUREZOqIcGDvvT/SLe69UVk33LRE9kqBiIhMUZEN7GCfxB5ZntR0lCtPzFLrWkREppbIBvb+WtimvdLCtmdpZTMREZlaIhnYxpgx17BHlyfdVQlsS4EtIiJTTDQD+02PvSDABAarq7LZhztX17BFRGRqiWZgm7GR7fkGr8vDlA3lDDg1CmwREZlaqppMW7Zs4ec//zmJRIIXXniB6667juXLl1ezCMDY69dQWZ7U6yhjjKFYD66tva9FRGRqqVpg+77PN77xDb7//e9j2zaXX345rjs5Ldk3t7D9wOB1ljEGSrUWrhvJjgcREZnCqpaYL7/8MsYY7r77bgqFAvX19XzkIx+p1unHCIKxj8tegNfpYQwU68C1FdgiIjK1VC2w29raWLNmDf/0T/9ENpvlhhtuIBaLccUVVxzwPQ0NaVz3yMyJbm7Ojt7PFcrEE3s/erY2SWIwh21bBDNcmptrxnx9FEX98x8K1dWhUX2Nn+pq/KJQV1UL7Ewmw+LFi8lmK5V66qmn8vzzz79tYPf05I7IuZubs3R1DYw+zhU8SkVv9HF39xBsHcLzAoaShoG+PF1OdK9jv7m+5MBUV4dG9TV+qqvxm0519XZ/eFSt7/ed73wnvb29+H5l6lRbWxuLFi2q1unHCN48StwLhq9hG0q14EQ4rEVEZGqqWgu7vr6eG264gZtuuomGhgb27NnD5z//+Wqdfoy3TOvq8wmKBi8BftLCdXQNW0REppaqDtO+4IILuOCCC6p5yv1687SuYHele7xYW3mswBYRkakmksn05ha26XpzYKtLXEREppZIBnbwpha26a5cV8/XVB6rhS0iIlNNJJPpzS1sdntgDIWswbIsHK10JiIiU0xEA/tNT/QGlVXOaiphbVkKbBERmVoiGthjE9vq8zFAuQYtSyoiIlNSJNNpzMqkxmD1BwSmslOXNv4QEZGpKJKBbfYZdeYUwZSBhEUQt3A04ExERKagSKbTviudxYYqXeReTaVlnU3HJqtYIiIiBxTJwB7Ja8uyKoEN5JKVjvLm+tTkFUxEROQAIhnYIy3seMwmNmgIAsNgrDIXW4EtIiJTUVWXJp0qRlrYjdkkdsGj7AXsoQxAc11yEksmIiKyf5FsYY9M63Ici2YSAOQSPrZt0VCbmMyiiYiI7FdEA7tya1kWjVQGmZUzFk21SRw7klUiIiJTXCTTaeQatm1BbblyVaBcAzN0/VpERKaoSAb2SAvbtizsgYCYa1PKQHO9rl+LiMjUFNFBZ5XEtjxDMBRQX5egbqbFkrl1k1wyERGR/YtoYFdu3cHKbf3cFFdduGjSyiMiInIwkewSH7mG7QxVFktx6iL5d4uIiIRIJAN7dFpXofLYrnUmsTQiIiIHF8nAHtn7w8kN3yqwRURkiotkYI+2sHOVLnE7G8lqEBGREIlkUo1O68pXbp2sWtgiIjK1RTSwhxdOyQ3fKrBFRGSKO2hg//a3v61GOaoqeFNg6xq2iIhMdQedz/SXf/mXOI7DFVdcweWXX05LS0s1yjWhRgadqYUtIiJhcdAW9i9/+Uv+7u/+ji1btnDxxRfz2c9+locffhjP86pRvgkxutJZfriFrcAWEZEpblzXsN/97ndz88038+STT3LBBRdw1113cc455/AP//APE12+CTE66GxouIWtLnEREZniDmnQWSaT4Y//+I/53Oc+x+zZs7n33nsnqlwTyhiDXTZYnsGKWVgJa7KLJCIi8rbGvSbnli1b+OlPf8pDDz3EzJkzueKKK7j00ksnsmwTxpi9q5w5WQfLUmCLiMjUdtDAvvfee7nvvvvYvn07l1xyCf/2b//GsmXLqlG2CRMYg5sHCw04ExGRcDhoYP/yl7/kU5/6FOeffz7FYpHW1tZqlGtCGQNuAcDSgDMREQmFg17D/rd/+zcuvPBCnnnmGf7oj/6I6667DoCXX36Za665ZsILOBEqLWyDZWnAmYiIhMO4B53deuut/OQnP6G2thaAd7zjHWzfvn3CCjaRjDHDLWwtmiIiIuFwSKPEm5ubxzyOx+NHtDDVYgy4w+uIa+MPEREJg3GnVSaTYffu3aMjqp977jmy2eyEFWwijQ46szToTEREwmHc07puuOEGrr76anbu3MlVV13Ftm3b+N73vjeRZZswxoBTrNx3ahTYIiIy9Y07sJcvX85//Md/sHr1agBOPvnk0evZYWOMwSmayrSujLrERURk6ht3YANks1nOPffciSpL1RgDTgmwLKyUAltERKa+gwb2Pffc87avf/zjHz9ihamWwBjs8vDCKWkFtoiITH0HDeyvfe1rnHDCCRxzzDHVKE9VjF7DToCd1jVsERGZ+g4a2DfddBP3338/mzZt4oMf/CCXXHIJdXV11SjbhAmCALcMJMFWl7iIiITAQQP7iiuu4IorrmDHjh088MADfPSjH+WYY47h2muvDe2a4qYMlg+WY2HFtPGHiIhMfeNuXs6fP59PfvKTfOITn+D555/n5ZdfnshyTaxCULlNKaxFRCQcDtrCNsbw1FNPcd9997Fp0yYuuugifvSjHzF//vxqlG9iDAe2lVR3uIiIhMNBA/ucc84Z3f/685//PJZlUSwWef311wE4+uijJ7yQR9zwOuJqYYuISFgcNLBjsRg9PT3ccccd3HnnnRhjRl+zLIvHHntsQgs4IUZa2BpwJiIiIXHQwP7Vr35VjXJUV6HyR4e6xEVEJCwOmlj/+I//yEsvvVSNslSNNRLYWjRFRERC4qCJtWzZMv793/+dCy64gBtvvJFf/epXFIvFapRtwowEtuZgi4hIWBy0S/yyyy7jsssuo1Qq8Zvf/IbHHnuMm266iWOPPZbzzz+fFStW0NjYWI2yHjnF4Ra2Bp2JiEhIjHvzj3g8zrnnnsu5556LMYa1a9fyy1/+kjvvvJOf/exnE1nGI05d4iIiEja/V2JZlsVJJ53EDTfccEhhXSgUuPTSS7n55pt/n9MeMaNd4kmtIy4iIuFQ1SbmLbfcwvHHH1/NU+5fcXhaV1pd4iIiEg7j7hJfvXr1AV875ZRTDvr+Bx54gFNOOYWNGzeSy+XGe9oJYQ2PmbNSamGLiEg4jDuwn3nmGQDWrl1LEASccMIJrFu3jng8zne/+923fe/rr7/Oli1buP7669m4ceO4C9fQkMZ1j0yoNjdnR+/HfRvftmhoSY95XvZSvYyf6urQqL7GT3U1flGoq3EH9sqVKwH47Gc/yx133DH6/NVXX33Q9z766KPE43FWrVrFiy++SLlc5q677uKTn/zk276vp+fItMSbm7N0dQ2MPvYHPYLAMOCVxjwvFW+uLzkw1dWhUX2Nn+pq/KZTXb3dHx7jDuwRxWKRRx99lOOOO47169ePa072tddeO+b9uVzuoGE9kaziyDxsXcMWEZFwOORBZ7feeiutra3cfvvttLa2ctttt437vQ8//DAvvPACa9asmdSpYPbw3xh2WtewRUQkHA65hZ1MJlm6dCktLS0YY3jyySe59NJLx/Xe97///bz//e8/5EIeScY3WGUDFthJtbBFRCQcDrmF/ZnPfIYNGzbgeR6+7+N53kSUa8IE+QBjwI+D42jhFBERCYdDbmFnMhk+97nPTURZqsLkKnOwg1hlARgREZEwOOTABrj++utZunTpaOBdc801R7RQEynIB4DBj4PyWkREwuKQA/vTn/70mMdha6WaYqVLXC1sEREJk3EH9kMPPcRll13Giy++OBp0xhgsy+L000+fsAIeaaZUmdIVOGphi4hIeIw7sI855hgATj311AkrTDWYssEAgWthK7FFRCQkxj1MetmyZQCcfPLJ7N69m7Vr17J7925OOumkCSvcRAiGF00xrlrYIiISHoc8r+m6666js7OTE088kc7OTq677rqJKNeEMaUADAQuamGLiEhoHPKgM8/zRpcVfc973sNTTz11pMs0ofZ2iauFLSIi4THuwP7e976HZVlYlsXKlSs59thj2bhxI4lEYiLLd8SZogFjCFxLo8RFRCQ0DhrY1113HbfddhunnnoqxWJxzN7XZ5xxxoQWbiKYcoChcg3bVl6LiEhIHDSwd+zYAcC73vUurrjiCu67777R18azReZUY4r7TutSYouISDgc0qCzIAjGPH7wwQePaGGqwZRMZeEUXcMWEZEQOWhg79sKfXOL1Bhz5Es0wUxpeC1xjRIXEZEQOWiXeGtrK1/5yld4xzveQT6fp1wuE4vFgHB2KQelfedhh6/8IiISTQcN7FWrVrFu3TrWrl1LLBbj1FNPZc6cORxzzDF0dHRUo4xHVFCstLCNq7AWEZHwOGhgn3baaZx22mmjj0ulEhs2bODVV1+lrq5uQgs3EYLhLnETm+SCiIiIHIJDXjglHo+zfPlyli9fPhHlmXAjo8SJqYUtIiLhcchLk4bd3ha2AltERMIjcoE9sr2mWtgiIhImkQvs0VHiCmwREQmRyAW2GR4ljgadiYhIiEQusAN1iYuISAhFLrCNpnWJiEgIRSqwjTEadCYiIqEUqcDGAwwYB2xHgS0iIuERqcAOipW9sLW1poiIhE2kAtuUDRijjT9ERCR0ohXYJVNpYWsvbBERCZloBXZxn72wUWKLiEh4RCuwywZjhlvYkfrkIiISdpGKLVM0GAzGAdeJ1EcXEZGQi1RqmXIw2sJ2bHWJi4hIeEQrsIsjXeKWWtgiIhIqkUotUxqe1uWohS0iIuESscAORqd1OVrpTEREQiRagV02GGOGr2FH6qOLiEjIRSq1gtLeaV2uWtgiIhIikQpsUwxGW9gadCYiImESqdQy5crSpBp0JiIiYROtwB6Z1hVTYIuISLhEK7D3HXSmLnEREQmRSKVW5Rp2pUvcVQtbRERCJFqBXaqsJR64luZhi4hIqEQrsD1DYCBwNA9bRETCJVKpZYLK0qTYmoctIiLhEqnAxqvktbHVwhYRkXCJVGoZv9IlbmytJS4iIuESqcDGM4AZbmErsEVEJDwiFdgmGO4StzQPW0REwiVaqeUbzcMWEZFQilRgG68yD1td4iIiEjZutU60fft2brnlFo4//nja29upr69n5cqV1To9sE+XuK0ucRERCZeqBXZvby8XX3wx73vf+wC4+OKLWbFiBSeeeGK1ijDcJW4wtlY6ExGRcKlaYC9fvnzM4yAISKVS1To9UJnWZQxggat52CIiEiJVC+x9Pfroo5x11lksWbLkbb+uoSGN6zpH5JzNzVm6XBfLtnBTLi0tWZLxSfn4odDcnJ3sIoSG6urQqL7GT3U1flGoq6on1rPPPstzzz3HV77ylYN+bU9P7oics7k5S1fXAIVcGd8PKJU9evYM4eo69n6N1JccnOrq0Ki+xk91NX7Tqa7e7g+PqibW448/ztNPP82NN95IV1cXL730UjVPP9olbhyNEhcRkXCpWmC/8sorfOlLX2Lt2rV84hOf4M/+7M/YunVrtU4PgCkbAGzXxrIU2CIiEh5V6xI/8cQTq96ifrPArwS2E1NYi4hIuETmIq4JTGV7TQscNzIfW0REponoJJevrTVFRCS8IpNcZmTRFAtcLZoiIiIhE53A9gyGkRHikfnYIiIyTUQnuQJGW9hallRERMImMoE9Zg62AltEREImMoGNt3cdcXWJi4hI2EQmuUzA6F7YrlY5ExGRkIlMYI+0sCt7YSuwRUQkXCIT2KPXsDUPW0REQig6yeUPjxJXC1tEREIoMoFt/OF52LqGLSIiIRStwB5tYUfmY4uIyDQRneQas5a4WtgiIhIukQnskUFn2OCqhS0iIiETneTyzeg8bLWwRUQkbCIT2Ga0S9zSKHEREQmdyAQ2+ww6U5e4iIiETWSSa+zCKWphi4hIuEQmsPGH1xK3FNgiIhI+kQlsM2Yt8ch8bBERmSYik1wm2BvYMQW2iIiETHSSa5+1xGNudD62iIhMD5FJLuMZAmNAgS0iIiEUneTyDUGgFraIiIRTZJLLBIzu1qXAFhGRsIlOcu3Two4rsEVEJGQik1yBF4yOEtdKZyIiEjaRSS6/bACwYzaWpYVTREQkXCIT2F45AMCOKaxFRCR8IhPYfqkS2K6rwBYRkfCJTGCPtLAd15nkkoiIiBy6yAT2SAvbiauFLSIi4ROdwN5n0JmIiEjYRCa9fG/4GrYCW0REQigy6TXaJa5R4iIiEkKRCezAq3SJu/HIfGQREZlGIpNeI13ijpYlFRGREIpMeo22sBOR+cgiIjKNRCa9RkaJuzHNwxYRkfCJTGAHGiUuIiIhFpn0Upe4iIiEWWTSKxhemjSmUeIiIhJCkUkv41da2ApsEREJo8ikl+Zhi4hImEUmvUYCOxbXKHEREQmfyAS2GQnshAJbRETCJ0KBXblVC1tERMIoOoGtQWciIhJikUivIDCjga1BZyIiEkaRSK+S52MZsG0L29X2miIiEj5uNU/2zDPP8Mgjj9DU1IRlWaxcubIq5y17AZYPlgU4CmwREQmfqgV2Pp/nb/7mb/j5z39OPB7nuuuu4ze/+Q3vec97JvzcpfJwC9uysBTYIiISQlXrEl+zZg1z5swhHo8DcMopp/D4449X5dxlL8AKhlvYVe1TEBEROTKqFl/d3d1kMpnRxzU1NXR3d7/texoa0rju4U/DKpV8MskY2UycmTNrD/t4UdDcnJ3sIoSG6urQqL7GT3U1flGoq6oFdlNTE0NDQ6OPBwcHaWpqetv39PTkjsy56zLMbEhhuRZdXQNH5JjTWXNzVvU0TqqrQ6P6Gj/V1fhNp7p6uz88qtYlftJJJ9HW1kapVAJg9erVrFixoirnHpnSZWnNFBERCamqtbBTqRR/+7d/y9///d/T0NDAscceW5UBZ7A3sDVCXEREwqqqQ7DOPPNMzjzzzGqessKv3GiEuIiIhFUkFk4Z2fgDdYmLiEhIRSOwR65ha5UzEREJqWgFtrrERUQkpKIR2CNd4pH4tCIiMh1FIsLUJS4iImEXjcD2NK1LRETCLRKBHWuMYTkWsZbYZBdFRETk9xKJrTBijTHm3roQO6N5XSIiEk6RCGwApy4yH1VERKahSHSJi4iIhJ0CW0REJAQU2CIiIiGgwBYREQkBBbaIiEgIKLBFRERCQIEtIiISAgpsERGREFBgi4iIhIACW0REJAQU2CIiIiFgGWPMZBdCRERE3p5a2CIiIiGgwBYREQkBBbaIiEgIKLBFRERCQIEtIiISAgpsERGREHAnuwAT7ZlnnuGRRx6hqakJy7JYuXLlZBdpSvnIRz5CIpEAwLZtfvCDH9Db28u3vvUt5s+fz7Zt27j++uuZMWPGJJd0cnR1dXHLLbewYcMGfvrTnwJQLBa5+eabaWlpYdu2bXz2s5/lqKOOAuDBBx9k/fr12LbNggUL+OhHPzqZxa+q/dXVfffdx3/913+Nfo996EMf4vLLLweiXVfbt2/nlltu4fjjj6e9vZ36+npWrlz5tj97t99+O4ODg/T393PmmWdy/vnnT/KnqJ4D1ddtt93G888/P/p111xzDWeeeSYwTevLTGO5XM68733vM8Vi0RhjzMqVK80zzzwzyaWaWm699da3PPfXf/3X5uc//7kxxpjHHnvM3HDDDdUu1pTxi1/8wjz22GPmgx/84Ohz//qv/2pWrVpljDFmw4YN5sorrzTGGLNr1y5z2WWXmSAIjDHGXHHFFWbr1q1VL/Nk2V9d/fSnPzU7dux4y9dGva7Wrl1rHn300dHHF110kXn55ZcP+LO3Zs0a85nPfMYYY0y5XDYXXHCB6e/vr37BJ8mB6mt/v7+Mmb71Na27xNesWcOcOXOIx+MAnHLKKTz++OOTW6gp5rXXXmPVqlXcdttto3XzxBNPcPLJJwOVOnviiScmsYST68ILLySTyYx57vHHHx+tn2OPPZYNGzYwODjIU089xQknnIBlWQCcfPLJPPnkk1Uv82TZX10B3HPPPdxxxx38y7/8C729vQCRr6vly5fzvve9b/RxEASkUqkD/uz9+te/5qSTTgLAdV0WL148pmU53R2ovgC+973vcccdd7Bq1Sry+TwwfetrWneJd3d3j/kFUlNTQ3d39ySWaOq5+uqrWb58Ob7v8/GPf5xMJjOm3mpqaujr68PzPFx3Wn+7jNuBvq/27Nkz5vmRuoyy008/nRUrVtDY2MgTTzzBF7/4RX7wgx+orvbx6KOPctZZZ7FkyZID/uzt2bOHxYsXj76npqaGPXv2TFaRJ9W+9XXhhRcyd+5c0uk099xzD1/72te46aabpm19TesWdlNTE0NDQ6OPBwcHaWpqmsQSTT3Lly8HwHEcTjvtNJ577rkx9TY4OEhdXZ3Ceh8H+r5qbGwc8/zQ0FDkv9/mz59PY2MjAO9+97t54YUX8H1fdTXs2Wef5bnnnuMrX/kKwAF/9t5cX4ODg6P1GiVvrq+lS5eSTqeByvfXs88+CzBt62taB/ZJJ51EW1sbpVIJgNWrV7NixYrJLdQUsnnzZn784x+PPm5tbWX+/Pmce+65vPTSS0Clzs4999zJKuKUtGLFitH62bhxI8uWLaOmpoazzz6bV199FTO8PP9LL73EOeecM5lFnXTf+ta38DwPgG3btjF37lwcx1FdUbm08vTTT3PjjTfS1dXFSy+9dMCfvRUrVrBmzRoAyuUyW7Zs4fTTT5+0sk+G/dXXzTffPPp6a2srCxYsAKZvfU37zT/+93//l4cffpiGhgZisZhGie+jo6ODr33taxx33HEMDg7ieR5/9Vd/RX9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\n",
       "text/plain": [
        "<Figure size 576x396 with 1 Axes>"
       ]
@@ -2184,7 +3082,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.8.3"
   }
  },
  "nbformat": 4,
diff --git a/doc/pub/How2ReadData/ipynb/Results/FigureFiles/Masses2016.png b/doc/pub/How2ReadData/ipynb/Results/FigureFiles/Masses2016.png
index 47a5a2bd0..c8c5989b1 100644
Binary files a/doc/pub/How2ReadData/ipynb/Results/FigureFiles/Masses2016.png and b/doc/pub/How2ReadData/ipynb/Results/FigureFiles/Masses2016.png differ
diff --git a/doc/pub/Intro2Course/html/._Intro2Course-bs000.html b/doc/pub/Intro2Course/html/._Intro2Course-bs000.html
index 01b5f208a..c788d6e82 100644
--- a/doc/pub/Intro2Course/html/._Intro2Course-bs000.html
+++ b/doc/pub/Intro2Course/html/._Intro2Course-bs000.html
@@ -44,26 +44,27 @@ Automatically generated HTML file from DocOnce source
  'sections': [('Overview of first week', 2, None, '___sec0'),
               ('Lectures and ComputerLab', 2, None, '___sec1'),
               ('Course Format', 2, None, '___sec2'),
-              ('Teachers and ComputerLab', 2, None, '___sec3'),
+              ('Teachers', 2, None, '___sec3'),
               ('Deadlines for projects (tentative)', 2, None, '___sec4'),
-              ('Learning outcomes', 2, None, '___sec5'),
+              ('Prerequisites', 2, None, '___sec5'),
+              ('Learning outcomes', 2, None, '___sec6'),
               ('Topics covered in this course: Statistical analysis and '
                'optimization of data',
                2,
                None,
-               '___sec6'),
+               '___sec7'),
               ('Topics covered in this course: Machine Learning',
                2,
                None,
-               '___sec7'),
+               '___sec8'),
               ('Extremely useful tools, strongly recommended',
                2,
                None,
-               '___sec8'),
+               '___sec9'),
               ('Other courses on Data science and Machine Learning  at UiO',
                2,
                None,
-               '___sec9')]}
+               '___sec10')]}
 end of tocinfo -->
 
 <body>
@@ -88,13 +89,14 @@ end of tocinfo -->
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs001.html#___sec0" style="font-size: 80%;">Overview of first week</a></li>
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs002.html#___sec1" style="font-size: 80%;">Lectures and ComputerLab</a></li>
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers and ComputerLab</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
 
         </ul>
       </li>
@@ -129,7 +131,7 @@ end of tocinfo -->
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Nov 12, 2019</h4></center> <!-- date -->
+<center><h4>Aug 19, 2020</h4></center> <!-- date -->
 <br>
 <p>
 
@@ -153,7 +155,7 @@ end of tocinfo -->
   <li><a href="._Intro2Course-bs008.html">9</a></li>
   <li><a href="._Intro2Course-bs009.html">10</a></li>
   <li><a href="">...</a></li>
-  <li><a href="._Intro2Course-bs010.html">11</a></li>
+  <li><a href="._Intro2Course-bs011.html">12</a></li>
   <li><a href="._Intro2Course-bs001.html">&raquo;</a></li>
 </ul>
 <!-- ------------------- end of main content --------------- -->
@@ -171,7 +173,7 @@ end of tocinfo -->
 
 
 <center style="font-size:80%">
-<!-- copyright --> &copy; 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
+<!-- copyright --> &copy; 1999-2020, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
 </center>
 
 
diff --git a/doc/pub/Intro2Course/html/._Intro2Course-bs001.html b/doc/pub/Intro2Course/html/._Intro2Course-bs001.html
index 42aeb8751..b2726f185 100644
--- a/doc/pub/Intro2Course/html/._Intro2Course-bs001.html
+++ b/doc/pub/Intro2Course/html/._Intro2Course-bs001.html
@@ -44,26 +44,27 @@ Automatically generated HTML file from DocOnce source
  'sections': [('Overview of first week', 2, None, '___sec0'),
               ('Lectures and ComputerLab', 2, None, '___sec1'),
               ('Course Format', 2, None, '___sec2'),
-              ('Teachers and ComputerLab', 2, None, '___sec3'),
+              ('Teachers', 2, None, '___sec3'),
               ('Deadlines for projects (tentative)', 2, None, '___sec4'),
-              ('Learning outcomes', 2, None, '___sec5'),
+              ('Prerequisites', 2, None, '___sec5'),
+              ('Learning outcomes', 2, None, '___sec6'),
               ('Topics covered in this course: Statistical analysis and '
                'optimization of data',
                2,
                None,
-               '___sec6'),
+               '___sec7'),
               ('Topics covered in this course: Machine Learning',
                2,
                None,
-               '___sec7'),
+               '___sec8'),
               ('Extremely useful tools, strongly recommended',
                2,
                None,
-               '___sec8'),
+               '___sec9'),
               ('Other courses on Data science and Machine Learning  at UiO',
                2,
                None,
-               '___sec9')]}
+               '___sec10')]}
 end of tocinfo -->
 
 <body>
@@ -88,13 +89,14 @@ end of tocinfo -->
      <!-- navigation toc: --> <li><a href="#___sec0" style="font-size: 80%;">Overview of first week</a></li>
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs002.html#___sec1" style="font-size: 80%;">Lectures and ComputerLab</a></li>
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers and ComputerLab</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
 
         </ul>
       </li>
@@ -118,10 +120,10 @@ end of tocinfo -->
 <p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
 
 <ul>
-  <li> Thursday August 22: First lecture: Presentation of the course, aims and content</li>
-  <li> Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra</li>
-  <li> Friday August 23: Linear regression</li>   
-  <li> Computer lab: Tuesday. First time: Tuesday August 27.</li> 
+  <li> Thursday August 20: First lecture: Presentation of the course, aims and content</li>
+  <li> Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra and elements of statistics</li>
+  <li> Friday August 21: Linear regression</li>   
+  <li> Computer lab: Wednesdays, 8am-6pm. First time: Wednesday August 26.</li> 
 </ul>
 </div>
 </div>
@@ -143,6 +145,8 @@ end of tocinfo -->
   <li><a href="._Intro2Course-bs008.html">9</a></li>
   <li><a href="._Intro2Course-bs009.html">10</a></li>
   <li><a href="._Intro2Course-bs010.html">11</a></li>
+  <li><a href="">...</a></li>
+  <li><a href="._Intro2Course-bs011.html">12</a></li>
   <li><a href="._Intro2Course-bs002.html">&raquo;</a></li>
 </ul>
 <!-- ------------------- end of main content --------------- -->
diff --git a/doc/pub/Intro2Course/html/._Intro2Course-bs002.html b/doc/pub/Intro2Course/html/._Intro2Course-bs002.html
index ee207360e..fd8b0bbc7 100644
--- a/doc/pub/Intro2Course/html/._Intro2Course-bs002.html
+++ b/doc/pub/Intro2Course/html/._Intro2Course-bs002.html
@@ -44,26 +44,27 @@ Automatically generated HTML file from DocOnce source
  'sections': [('Overview of first week', 2, None, '___sec0'),
               ('Lectures and ComputerLab', 2, None, '___sec1'),
               ('Course Format', 2, None, '___sec2'),
-              ('Teachers and ComputerLab', 2, None, '___sec3'),
+              ('Teachers', 2, None, '___sec3'),
               ('Deadlines for projects (tentative)', 2, None, '___sec4'),
-              ('Learning outcomes', 2, None, '___sec5'),
+              ('Prerequisites', 2, None, '___sec5'),
+              ('Learning outcomes', 2, None, '___sec6'),
               ('Topics covered in this course: Statistical analysis and '
                'optimization of data',
                2,
                None,
-               '___sec6'),
+               '___sec7'),
               ('Topics covered in this course: Machine Learning',
                2,
                None,
-               '___sec7'),
+               '___sec8'),
               ('Extremely useful tools, strongly recommended',
                2,
                None,
-               '___sec8'),
+               '___sec9'),
               ('Other courses on Data science and Machine Learning  at UiO',
                2,
                None,
-               '___sec9')]}
+               '___sec10')]}
 end of tocinfo -->
 
 <body>
@@ -88,13 +89,14 @@ end of tocinfo -->
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs001.html#___sec0" style="font-size: 80%;">Overview of first week</a></li>
      <!-- navigation toc: --> <li><a href="#___sec1" style="font-size: 80%;">Lectures and ComputerLab</a></li>
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers and ComputerLab</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
 
         </ul>
       </li>
@@ -118,12 +120,10 @@ end of tocinfo -->
 <p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
 
 <ul>
-  <li> Lectures: Thursday (2.15pm-4pm, this may change) and Friday (12.15pm-2pm).</li>
-  <li> Weekly reading assignments needed to solve projects and exercises.</li>
+  <li> Lectures: Thursday (12.15pm-2pm  and Friday (12.15pm-2pm).  Due to the present COVID-19 situation all lectures will be online. They will be recorded and posted online at the official UiO <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/index.html" target="_self">website</a>.</li>
+  <li> Weekly reading assignments and videos needed to solve projects and exercises.</li>
   <li> Weekly exercises when not working on projects. You can hand in exercises if you want.</li>
-  <li> First hour of each lab session may be used to discuss technicalities, address questions etc linked with projects and exercises.</li>
   <li> Detailed lecture notes, exercises, all programs presented, projects etc can be found at the homepage of the course.</li>
-  <li> Computerlab: Tuesday (8am-4pm), VB IT-auditorium 3. Depending on how many enlist we may extend the lab sessions</li>
   <li> Weekly plans and all other information are on the official webpage.</li>
   <li> No final exam, three projects that are graded and have to be approved.</li> 
 </ul>
@@ -147,6 +147,7 @@ end of tocinfo -->
   <li><a href="._Intro2Course-bs008.html">9</a></li>
   <li><a href="._Intro2Course-bs009.html">10</a></li>
   <li><a href="._Intro2Course-bs010.html">11</a></li>
+  <li><a href="._Intro2Course-bs011.html">12</a></li>
   <li><a href="._Intro2Course-bs003.html">&raquo;</a></li>
 </ul>
 <!-- ------------------- end of main content --------------- -->
diff --git a/doc/pub/Intro2Course/html/._Intro2Course-bs003.html b/doc/pub/Intro2Course/html/._Intro2Course-bs003.html
index d19f5a2d9..6d003db48 100644
--- a/doc/pub/Intro2Course/html/._Intro2Course-bs003.html
+++ b/doc/pub/Intro2Course/html/._Intro2Course-bs003.html
@@ -44,26 +44,27 @@ Automatically generated HTML file from DocOnce source
  'sections': [('Overview of first week', 2, None, '___sec0'),
               ('Lectures and ComputerLab', 2, None, '___sec1'),
               ('Course Format', 2, None, '___sec2'),
-              ('Teachers and ComputerLab', 2, None, '___sec3'),
+              ('Teachers', 2, None, '___sec3'),
               ('Deadlines for projects (tentative)', 2, None, '___sec4'),
-              ('Learning outcomes', 2, None, '___sec5'),
+              ('Prerequisites', 2, None, '___sec5'),
+              ('Learning outcomes', 2, None, '___sec6'),
               ('Topics covered in this course: Statistical analysis and '
                'optimization of data',
                2,
                None,
-               '___sec6'),
+               '___sec7'),
               ('Topics covered in this course: Machine Learning',
                2,
                None,
-               '___sec7'),
+               '___sec8'),
               ('Extremely useful tools, strongly recommended',
                2,
                None,
-               '___sec8'),
+               '___sec9'),
               ('Other courses on Data science and Machine Learning  at UiO',
                2,
                None,
-               '___sec9')]}
+               '___sec10')]}
 end of tocinfo -->
 
 <body>
@@ -88,13 +89,14 @@ end of tocinfo -->
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs001.html#___sec0" style="font-size: 80%;">Overview of first week</a></li>
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs002.html#___sec1" style="font-size: 80%;">Lectures and ComputerLab</a></li>
      <!-- navigation toc: --> <li><a href="#___sec2" style="font-size: 80%;">Course Format</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers and ComputerLab</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
 
         </ul>
       </li>
@@ -150,6 +152,7 @@ end of tocinfo -->
   <li><a href="._Intro2Course-bs008.html">9</a></li>
   <li><a href="._Intro2Course-bs009.html">10</a></li>
   <li><a href="._Intro2Course-bs010.html">11</a></li>
+  <li><a href="._Intro2Course-bs011.html">12</a></li>
   <li><a href="._Intro2Course-bs004.html">&raquo;</a></li>
 </ul>
 <!-- ------------------- end of main content --------------- -->
diff --git a/doc/pub/Intro2Course/html/._Intro2Course-bs004.html b/doc/pub/Intro2Course/html/._Intro2Course-bs004.html
index dd038401d..afd7ac110 100644
--- a/doc/pub/Intro2Course/html/._Intro2Course-bs004.html
+++ b/doc/pub/Intro2Course/html/._Intro2Course-bs004.html
@@ -44,26 +44,27 @@ Automatically generated HTML file from DocOnce source
  'sections': [('Overview of first week', 2, None, '___sec0'),
               ('Lectures and ComputerLab', 2, None, '___sec1'),
               ('Course Format', 2, None, '___sec2'),
-              ('Teachers and ComputerLab', 2, None, '___sec3'),
+              ('Teachers', 2, None, '___sec3'),
               ('Deadlines for projects (tentative)', 2, None, '___sec4'),
-              ('Learning outcomes', 2, None, '___sec5'),
+              ('Prerequisites', 2, None, '___sec5'),
+              ('Learning outcomes', 2, None, '___sec6'),
               ('Topics covered in this course: Statistical analysis and '
                'optimization of data',
                2,
                None,
-               '___sec6'),
+               '___sec7'),
               ('Topics covered in this course: Machine Learning',
                2,
                None,
-               '___sec7'),
+               '___sec8'),
               ('Extremely useful tools, strongly recommended',
                2,
                None,
-               '___sec8'),
+               '___sec9'),
               ('Other courses on Data science and Machine Learning  at UiO',
                2,
                None,
-               '___sec9')]}
+               '___sec10')]}
 end of tocinfo -->
 
 <body>
@@ -88,13 +89,14 @@ end of tocinfo -->
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs001.html#___sec0" style="font-size: 80%;">Overview of first week</a></li>
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs002.html#___sec1" style="font-size: 80%;">Lectures and ComputerLab</a></li>
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
-     <!-- navigation toc: --> <li><a href="#___sec3" style="font-size: 80%;">Teachers and ComputerLab</a></li>
+     <!-- navigation toc: --> <li><a href="#___sec3" style="font-size: 80%;">Teachers</a></li>
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
 
         </ul>
       </li>
@@ -110,7 +112,7 @@ end of tocinfo -->
 <a name="part0004"></a>
 <!-- !split -->
 
-<h2 id="___sec3" class="anchor">Teachers and ComputerLab </h2>
+<h2 id="___sec3" class="anchor">Teachers  </h2>
 
 <p>
 <div class="panel panel-default">
@@ -120,31 +122,26 @@ end of tocinfo -->
 <p>
 <b>Teachers :</b>
 
-<ol>
-<li> <a href="https://www.researchgate.net/profile/Hanna_Svennevik" target="_self">Hanna Svennevik</a></li>
-<li> <a href="http://mhjgit.github.io/info/doc/web/" target="_self">Morten Hjorth-Jensen</a></li>
-<li> <a href="https://no.linkedin.com/in/lucas-charpentier-176206171" target="_self">Lucas Charpentier</a></li>
-<li> <a href="https://www.researchgate.net/profile/Stian_Bilek" target="_self">Stian Bilek</a></li>
-<li> <a href="https://github.com/Schoyen" target="_self">&#216;yvind Sigmundson Sch&#248;yen</a></li>
-</ol>
+<ul>
+<li> Morten Hjorth-Jensen, morten.hjorth-jensen@fys.uio.no</li>
 
+<ul>
+  <li> <b>Phone</b>: +47-48257387</li>
+  <li> <b>Office</b>: Department of Physics, University of Oslo, Eastern wing, room F&#216;470</li> 
+  <li> <b>Office hours</b>: <em>Anytime</em>! In Fall Semester 2020 (FS20), as a rule of thumb office hours are planned via computer or telephone. Individual or group office hours will be performed via zoom. Feel free to send an email for planning. In person meetings may also be possible if allowed by the University of Oslo's COVID-19 instructions.</li>
+</ul>
 
-<div class="row">
-  <div class="col-xs-3">
-    <table class="table table-striped table-hover table-condensed">
-<thead>
-<tr><td align="center"><b>      day       </b></td> <td align="center"><b>   Time  </b></td> </tr>
-</thead>
-<tbody>
-<tr><td align="center">   Group 1: Tuesday    </td> <td align="center">   8am-10am     </td> </tr>
-<tr><td align="center">   Group 2: Tuesday    </td> <td align="center">   10am-12pm    </td> </tr>
-<tr><td align="center">   Group 3: Tuesday    </td> <td align="center">   12pm-2pm     </td> </tr>
-<tr><td align="center">   Group 4: Tuesday    </td> <td align="center">   2pm-4pm      </td> </tr>
-</tbody>
-    </table>
-  </div> <!-- col-xs-3 -->
-</div> <!-- cell row -->
-<p>
+<li> &#216;yvind Sigmundson Sch&#248;yen, oyvinssc@student.matnat.uio.no</li>
+
+<ul>
+  <li> <b>Office</b>: Department of Physics, University of Oslo, Eastern wing, room F&#216;452</li>
+</ul>
+
+<li> Michael Bitney, m.s.bitney@fys.uio.no</li>
+<li> Kristian Wold, kriswold@student.matnat.uio.no</li>
+<li> Nicolai Haug, nicoha@student.matnat.uio.no</li>
+<li> Per-Dimitri S&#248;nsteland, perdimitri.bs@gmail.com</li>
+</ul>
 </div>
 </div>
 
@@ -165,6 +162,7 @@ end of tocinfo -->
   <li><a href="._Intro2Course-bs008.html">9</a></li>
   <li><a href="._Intro2Course-bs009.html">10</a></li>
   <li><a href="._Intro2Course-bs010.html">11</a></li>
+  <li><a href="._Intro2Course-bs011.html">12</a></li>
   <li><a href="._Intro2Course-bs005.html">&raquo;</a></li>
 </ul>
 <!-- ------------------- end of main content --------------- -->
diff --git a/doc/pub/Intro2Course/html/._Intro2Course-bs005.html b/doc/pub/Intro2Course/html/._Intro2Course-bs005.html
index 475e130f1..d0cb88367 100644
--- a/doc/pub/Intro2Course/html/._Intro2Course-bs005.html
+++ b/doc/pub/Intro2Course/html/._Intro2Course-bs005.html
@@ -44,26 +44,27 @@ Automatically generated HTML file from DocOnce source
  'sections': [('Overview of first week', 2, None, '___sec0'),
               ('Lectures and ComputerLab', 2, None, '___sec1'),
               ('Course Format', 2, None, '___sec2'),
-              ('Teachers and ComputerLab', 2, None, '___sec3'),
+              ('Teachers', 2, None, '___sec3'),
               ('Deadlines for projects (tentative)', 2, None, '___sec4'),
-              ('Learning outcomes', 2, None, '___sec5'),
+              ('Prerequisites', 2, None, '___sec5'),
+              ('Learning outcomes', 2, None, '___sec6'),
               ('Topics covered in this course: Statistical analysis and '
                'optimization of data',
                2,
                None,
-               '___sec6'),
+               '___sec7'),
               ('Topics covered in this course: Machine Learning',
                2,
                None,
-               '___sec7'),
+               '___sec8'),
               ('Extremely useful tools, strongly recommended',
                2,
                None,
-               '___sec8'),
+               '___sec9'),
               ('Other courses on Data science and Machine Learning  at UiO',
                2,
                None,
-               '___sec9')]}
+               '___sec10')]}
 end of tocinfo -->
 
 <body>
@@ -88,13 +89,14 @@ end of tocinfo -->
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs001.html#___sec0" style="font-size: 80%;">Overview of first week</a></li>
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs002.html#___sec1" style="font-size: 80%;">Lectures and ComputerLab</a></li>
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers and ComputerLab</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
      <!-- navigation toc: --> <li><a href="#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
 
         </ul>
       </li>
@@ -118,12 +120,12 @@ end of tocinfo -->
 <p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
 
 <ol>
-<li> Project 1: September 30 (graded with feedback)</li>
-<li> Project 2: November 13 (graded with feedback)</li>
-<li> Project 3: December 15  (graded with feedback)</li>
+<li> Project 1: September 28 (graded with feedback)</li>
+<li> Project 2: November 2 (graded with feedback)</li>
+<li> Project 3: December 7  (graded with feedback)</li>
 </ol>
 
-Projects are handed in using devilry.ifi.uio.no. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via devilry.
+Projects are handed in using <b>Canvas</b>. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via <b>Canvas</b>.
 
 <p>
 </div>
@@ -146,6 +148,7 @@ Projects are handed in using devilry.ifi.uio.no. We use Github as repository for
   <li><a href="._Intro2Course-bs008.html">9</a></li>
   <li><a href="._Intro2Course-bs009.html">10</a></li>
   <li><a href="._Intro2Course-bs010.html">11</a></li>
+  <li><a href="._Intro2Course-bs011.html">12</a></li>
   <li><a href="._Intro2Course-bs006.html">&raquo;</a></li>
 </ul>
 <!-- ------------------- end of main content --------------- -->
diff --git a/doc/pub/Intro2Course/html/._Intro2Course-bs006.html b/doc/pub/Intro2Course/html/._Intro2Course-bs006.html
index 4c393bd84..670b68d1a 100644
--- a/doc/pub/Intro2Course/html/._Intro2Course-bs006.html
+++ b/doc/pub/Intro2Course/html/._Intro2Course-bs006.html
@@ -44,26 +44,27 @@ Automatically generated HTML file from DocOnce source
  'sections': [('Overview of first week', 2, None, '___sec0'),
               ('Lectures and ComputerLab', 2, None, '___sec1'),
               ('Course Format', 2, None, '___sec2'),
-              ('Teachers and ComputerLab', 2, None, '___sec3'),
+              ('Teachers', 2, None, '___sec3'),
               ('Deadlines for projects (tentative)', 2, None, '___sec4'),
-              ('Learning outcomes', 2, None, '___sec5'),
+              ('Prerequisites', 2, None, '___sec5'),
+              ('Learning outcomes', 2, None, '___sec6'),
               ('Topics covered in this course: Statistical analysis and '
                'optimization of data',
                2,
                None,
-               '___sec6'),
+               '___sec7'),
               ('Topics covered in this course: Machine Learning',
                2,
                None,
-               '___sec7'),
+               '___sec8'),
               ('Extremely useful tools, strongly recommended',
                2,
                None,
-               '___sec8'),
+               '___sec9'),
               ('Other courses on Data science and Machine Learning  at UiO',
                2,
                None,
-               '___sec9')]}
+               '___sec10')]}
 end of tocinfo -->
 
 <body>
@@ -88,13 +89,14 @@ end of tocinfo -->
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs001.html#___sec0" style="font-size: 80%;">Overview of first week</a></li>
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs002.html#___sec1" style="font-size: 80%;">Lectures and ComputerLab</a></li>
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers and ComputerLab</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
-     <!-- navigation toc: --> <li><a href="#___sec5" style="font-size: 80%;">Learning outcomes</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
+     <!-- navigation toc: --> <li><a href="#___sec5" style="font-size: 80%;">Prerequisites</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
 
         </ul>
       </li>
@@ -110,28 +112,18 @@ end of tocinfo -->
 <a name="part0006"></a>
 <!-- !split -->
 
-<h2 id="___sec5" class="anchor">Learning outcomes </h2>
+<h2 id="___sec5" class="anchor">Prerequisites </h2>
 
 <p>
-<div class="panel panel-default">
-<div class="panel-body">
-<p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
-
-<ul>
-<li> Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, data optimization and machine learning</li>
-<li> Be capable of extending the acquired knowledge to other systems and cases</li>
-<li> Have an understanding of central algorithms used in data analysis and machine learning</li>
-<li> Gain knowledge of central aspects of Monte Carlo methods, Markov chains, Gibbs samplers and their possible applications</li>
-<li> Understand linear methods for regression and classification, from ordinary least squares, via Lasso and Ridge to Logistic regression</li>
-<li> Learn about various neural networks and deep  learning methods for supervised and unsupervised learning</li>
-<li> Learn about about decision trees and random forests</li>
-<li> Learn about support vector machines and kernel transformations</li>
-<li> Reduction of data sets, from PCA to clustering, supervised and unsupervided methods</li>
-<li> Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++</li>
-</ul>
-</div>
-</div>
-
+Basic knowledge in programming and mathematics, with an emphasis on
+linear algebra. Knowledge of Python or/and C++ as programming
+languages is strongly recommended and experience with Jupiter notebook
+is recommended. Required courses are the equivalents to the University
+of Oslo mathematics courses MAT1100, MAT1110, MAT1120 and at least one
+of the corresponding computing and programming courses INF1000/INF1110
+or MAT-INF1100/MAT-INF1100L/BIOS1100/KJM-INF1100. Most universities
+offer nowadays a basic programming course (often compulsory) where
+Python is the recurring programming language.
 
 <p>
 <p>
@@ -149,6 +141,7 @@ end of tocinfo -->
   <li><a href="._Intro2Course-bs008.html">9</a></li>
   <li><a href="._Intro2Course-bs009.html">10</a></li>
   <li><a href="._Intro2Course-bs010.html">11</a></li>
+  <li><a href="._Intro2Course-bs011.html">12</a></li>
   <li><a href="._Intro2Course-bs007.html">&raquo;</a></li>
 </ul>
 <!-- ------------------- end of main content --------------- -->
diff --git a/doc/pub/Intro2Course/html/._Intro2Course-bs007.html b/doc/pub/Intro2Course/html/._Intro2Course-bs007.html
index be743e158..24443553c 100644
--- a/doc/pub/Intro2Course/html/._Intro2Course-bs007.html
+++ b/doc/pub/Intro2Course/html/._Intro2Course-bs007.html
@@ -44,26 +44,27 @@ Automatically generated HTML file from DocOnce source
  'sections': [('Overview of first week', 2, None, '___sec0'),
               ('Lectures and ComputerLab', 2, None, '___sec1'),
               ('Course Format', 2, None, '___sec2'),
-              ('Teachers and ComputerLab', 2, None, '___sec3'),
+              ('Teachers', 2, None, '___sec3'),
               ('Deadlines for projects (tentative)', 2, None, '___sec4'),
-              ('Learning outcomes', 2, None, '___sec5'),
+              ('Prerequisites', 2, None, '___sec5'),
+              ('Learning outcomes', 2, None, '___sec6'),
               ('Topics covered in this course: Statistical analysis and '
                'optimization of data',
                2,
                None,
-               '___sec6'),
+               '___sec7'),
               ('Topics covered in this course: Machine Learning',
                2,
                None,
-               '___sec7'),
+               '___sec8'),
               ('Extremely useful tools, strongly recommended',
                2,
                None,
-               '___sec8'),
+               '___sec9'),
               ('Other courses on Data science and Machine Learning  at UiO',
                2,
                None,
-               '___sec9')]}
+               '___sec10')]}
 end of tocinfo -->
 
 <body>
@@ -88,13 +89,14 @@ end of tocinfo -->
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs001.html#___sec0" style="font-size: 80%;">Overview of first week</a></li>
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs002.html#___sec1" style="font-size: 80%;">Lectures and ComputerLab</a></li>
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers and ComputerLab</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes</a></li>
-     <!-- navigation toc: --> <li><a href="#___sec6" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
+     <!-- navigation toc: --> <li><a href="#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
 
         </ul>
       </li>
@@ -110,22 +112,27 @@ end of tocinfo -->
 <a name="part0007"></a>
 <!-- !split -->
 
-<h2 id="___sec6" class="anchor">Topics covered in this course: Statistical analysis and optimization of data </h2>
+<h2 id="___sec6" class="anchor">Learning outcomes </h2>
 
 <p>
 <div class="panel panel-default">
 <div class="panel-body">
 <p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
 
+<p>
+This course aims at giving you insights and knowledge about  many of the central algorithms used in Data Analysis and Machine Learning.  The course is project based and through  various numerical projects, normally three, you will be exposed to fundamental research problems in these fields, with the aim to reproduce state of the art scientific results. Both supervised and unsupervised methods will be covered. The emphasis is on a frequentist approach, although we will try to link it with a Bayesian approach as well. You will learn to develop and structure large codes for studying different cases where Machine Learning is applied to, get acquainted with computing facilities and learn to handle large scientific projects. A good scientific and ethical conduct is emphasized throughout the course. More specifically, after this course you will
+
 <ul>
-<li> Basic concepts, expectation values, variance, covariance, correlation functions and errors</li>
-<li> Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions</li>
-<li> Central elements of Bayesian statistics and modeling</li>
-<li> Gradient methods for data optimization</li>
-<li> Monte Carlo methods, Markov chains, Metropolis-Hastings algorithm</li>
-<li> Linear methods for regression and classification</li>
-<li> Estimation of errors using cross-validation, blocking, bootstrapping and jackknife methods</li>
-<li> Practical optimization using Singular-value decomposition and least squares for parameterizing data</li>
+<li> Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, data optimization and machine learning;</li>
+<li> Be capable of extending the acquired knowledge to other systems and cases;</li>
+<li> Have an understanding of central algorithms used in data analysis and machine learning;</li>
+<li> Understand linear methods for regression and classification, from ordinary least squares, via Lasso and Ridge to Logistic regression;</li>
+<li> Learn about neural networks and deep  learning methods for supervised and unsupervised learning. Emphasis on feed forward neural networks, convolutional and recurrent neural networks;</li> 
+<li> Learn about about decision trees, random forests, bagging and boosting methods;</li>
+<li> Learn about support vector machines and kernel transformations;</li>
+<li> Reduction of data sets, from PCA to clustering;</li>
+<li> Autoencoders and Reinforcement Learning;</li>
+<li> Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++ and/or Fortran (Fortran2003 or later).</li>  
 </ul>
 </div>
 </div>
@@ -147,6 +154,7 @@ end of tocinfo -->
   <li><a href="._Intro2Course-bs008.html">9</a></li>
   <li><a href="._Intro2Course-bs009.html">10</a></li>
   <li><a href="._Intro2Course-bs010.html">11</a></li>
+  <li><a href="._Intro2Course-bs011.html">12</a></li>
   <li><a href="._Intro2Course-bs008.html">&raquo;</a></li>
 </ul>
 <!-- ------------------- end of main content --------------- -->
diff --git a/doc/pub/Intro2Course/html/._Intro2Course-bs008.html b/doc/pub/Intro2Course/html/._Intro2Course-bs008.html
index eee429942..90095b8d2 100644
--- a/doc/pub/Intro2Course/html/._Intro2Course-bs008.html
+++ b/doc/pub/Intro2Course/html/._Intro2Course-bs008.html
@@ -44,26 +44,27 @@ Automatically generated HTML file from DocOnce source
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               ('Lectures and ComputerLab', 2, None, '___sec1'),
               ('Course Format', 2, None, '___sec2'),
-              ('Teachers and ComputerLab', 2, None, '___sec3'),
+              ('Teachers', 2, None, '___sec3'),
               ('Deadlines for projects (tentative)', 2, None, '___sec4'),
-              ('Learning outcomes', 2, None, '___sec5'),
+              ('Prerequisites', 2, None, '___sec5'),
+              ('Learning outcomes', 2, None, '___sec6'),
               ('Topics covered in this course: Statistical analysis and '
                'optimization of data',
                2,
                None,
-               '___sec6'),
+               '___sec7'),
               ('Topics covered in this course: Machine Learning',
                2,
                None,
-               '___sec7'),
+               '___sec8'),
               ('Extremely useful tools, strongly recommended',
                2,
                None,
-               '___sec8'),
+               '___sec9'),
               ('Other courses on Data science and Machine Learning  at UiO',
                2,
                None,
-               '___sec9')]}
+               '___sec10')]}
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 <body>
@@ -88,13 +89,14 @@ end of tocinfo -->
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs001.html#___sec0" style="font-size: 80%;">Overview of first week</a></li>
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs002.html#___sec1" style="font-size: 80%;">Lectures and ComputerLab</a></li>
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers and ComputerLab</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
-     <!-- navigation toc: --> <li><a href="#___sec7" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
+     <!-- navigation toc: --> <li><a href="#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
 
         </ul>
       </li>
@@ -110,22 +112,34 @@ end of tocinfo -->
 <a name="part0008"></a>
 <!-- !split -->
 
-<h2 id="___sec7" class="anchor">Topics covered in this course: Machine Learning </h2>
+<h2 id="___sec7" class="anchor">Topics covered in this course: Statistical analysis and optimization of data </h2>
+
+<p>
+The course has two central parts
+
+<ol>
+<li> Statistical analysis and optimization of data</li>
+<li> Machine learning</li>
+</ol>
+
+These topics will be scattered thorughout the course and may not  necessarily be taught separately. Rather, we will often take an approach (during the lectures and project/exercise sessions) where say elements from statistical data analysis are mixed with specific Machine Learning algorithms
 
 <p>
 <div class="panel panel-default">
 <div class="panel-body">
 <p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
+
+<p>
 The following topics will be covered
 
 <ul>
-<li> Linear Regression and Logistic Regression</li>
-<li> Neural networks and deep learning</li>
-<li> Decisions trees and nearest neighbor algorithms</li>
-<li> Support vector machines</li>
-<li> Bayesian Neural Networks</li>
-<li> Boltzmann Machines</li>
-<li> Dimensionality reduction, from PCA to cluster models</li>
+<li> Basic concepts, expectation values, variance, covariance, correlation functions and errors;</li>
+<li> Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;</li>
+<li> Central elements of Bayesian statistics and modeling;</li>
+<li> Gradient methods for data optimization,</li> 
+<li> Monte Carlo methods, Markov chains, Gibbs sampling and Metropolis-Hastings sampling;</li>
+<li> Estimation of errors and resampling techniques such as the cross-validation, blocking, bootstrapping and jackknife methods;</li>
+<li> Principal Component Analysis (PCA) and its mathematical foundation</li>
 </ul>
 </div>
 </div>
@@ -147,6 +161,7 @@ The following topics will be covered
   <li class="active"><a href="._Intro2Course-bs008.html">9</a></li>
   <li><a href="._Intro2Course-bs009.html">10</a></li>
   <li><a href="._Intro2Course-bs010.html">11</a></li>
+  <li><a href="._Intro2Course-bs011.html">12</a></li>
   <li><a href="._Intro2Course-bs009.html">&raquo;</a></li>
 </ul>
 <!-- ------------------- end of main content --------------- -->
diff --git a/doc/pub/Intro2Course/html/._Intro2Course-bs009.html b/doc/pub/Intro2Course/html/._Intro2Course-bs009.html
index 0c39a8057..8d95f7716 100644
--- a/doc/pub/Intro2Course/html/._Intro2Course-bs009.html
+++ b/doc/pub/Intro2Course/html/._Intro2Course-bs009.html
@@ -44,26 +44,27 @@ Automatically generated HTML file from DocOnce source
  'sections': [('Overview of first week', 2, None, '___sec0'),
               ('Lectures and ComputerLab', 2, None, '___sec1'),
               ('Course Format', 2, None, '___sec2'),
-              ('Teachers and ComputerLab', 2, None, '___sec3'),
+              ('Teachers', 2, None, '___sec3'),
               ('Deadlines for projects (tentative)', 2, None, '___sec4'),
-              ('Learning outcomes', 2, None, '___sec5'),
+              ('Prerequisites', 2, None, '___sec5'),
+              ('Learning outcomes', 2, None, '___sec6'),
               ('Topics covered in this course: Statistical analysis and '
                'optimization of data',
                2,
                None,
-               '___sec6'),
+               '___sec7'),
               ('Topics covered in this course: Machine Learning',
                2,
                None,
-               '___sec7'),
+               '___sec8'),
               ('Extremely useful tools, strongly recommended',
                2,
                None,
-               '___sec8'),
+               '___sec9'),
               ('Other courses on Data science and Machine Learning  at UiO',
                2,
                None,
-               '___sec9')]}
+               '___sec10')]}
 end of tocinfo -->
 
 <body>
@@ -88,13 +89,14 @@ end of tocinfo -->
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs001.html#___sec0" style="font-size: 80%;">Overview of first week</a></li>
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs002.html#___sec1" style="font-size: 80%;">Lectures and ComputerLab</a></li>
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers and ComputerLab</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
-     <!-- navigation toc: --> <li><a href="#___sec8" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
+     <!-- navigation toc: --> <li><a href="#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
 
         </ul>
       </li>
@@ -110,18 +112,27 @@ end of tocinfo -->
 <a name="part0009"></a>
 <!-- !split -->
 
-<h2 id="___sec8" class="anchor">Extremely useful tools, strongly recommended </h2>
+<h2 id="___sec8" class="anchor">Topics covered in this course: Machine Learning </h2>
 
 <p>
 <div class="panel panel-default">
 <div class="panel-body">
 <p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
+The following topics will be covered
 
 <ul>
-  <li> GIT for version control, highly recommended</li>
-  <li> Devilry for handing in projects, next week</li>
-  <li> Anaconda and other Python environments, see intro slides</li>
+<li> Linear Regression and Logistic Regression;</li>
+<li> Neural networks and deep learning, including convolutional and recurrent neural networks</li>
+<li> Decisions trees, Random Forests, Bagging and Boosting</li>
+<li> Support vector machines</li>
+<li> Bayesian linear and logistic regression</li>
+<li> Boltzmann Machines</li>
+<li> Unsupervised learning Dimensionality reduction, from PCA to cluster models</li>
 </ul>
+
+Hands-on demonstrations, exercises and projects aim at deepening your understanding of these topics.
+
+<p>
 </div>
 </div>
 
@@ -142,6 +153,7 @@ end of tocinfo -->
   <li><a href="._Intro2Course-bs008.html">9</a></li>
   <li class="active"><a href="._Intro2Course-bs009.html">10</a></li>
   <li><a href="._Intro2Course-bs010.html">11</a></li>
+  <li><a href="._Intro2Course-bs011.html">12</a></li>
   <li><a href="._Intro2Course-bs010.html">&raquo;</a></li>
 </ul>
 <!-- ------------------- end of main content --------------- -->
diff --git a/doc/pub/Intro2Course/html/._Intro2Course-bs010.html b/doc/pub/Intro2Course/html/._Intro2Course-bs010.html
index b81279662..475c87d10 100644
--- a/doc/pub/Intro2Course/html/._Intro2Course-bs010.html
+++ b/doc/pub/Intro2Course/html/._Intro2Course-bs010.html
@@ -44,26 +44,27 @@ Automatically generated HTML file from DocOnce source
  'sections': [('Overview of first week', 2, None, '___sec0'),
               ('Lectures and ComputerLab', 2, None, '___sec1'),
               ('Course Format', 2, None, '___sec2'),
-              ('Teachers and ComputerLab', 2, None, '___sec3'),
+              ('Teachers', 2, None, '___sec3'),
               ('Deadlines for projects (tentative)', 2, None, '___sec4'),
-              ('Learning outcomes', 2, None, '___sec5'),
+              ('Prerequisites', 2, None, '___sec5'),
+              ('Learning outcomes', 2, None, '___sec6'),
               ('Topics covered in this course: Statistical analysis and '
                'optimization of data',
                2,
                None,
-               '___sec6'),
+               '___sec7'),
               ('Topics covered in this course: Machine Learning',
                2,
                None,
-               '___sec7'),
+               '___sec8'),
               ('Extremely useful tools, strongly recommended',
                2,
                None,
-               '___sec8'),
+               '___sec9'),
               ('Other courses on Data science and Machine Learning  at UiO',
                2,
                None,
-               '___sec9')]}
+               '___sec10')]}
 end of tocinfo -->
 
 <body>
@@ -88,13 +89,14 @@ end of tocinfo -->
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs001.html#___sec0" style="font-size: 80%;">Overview of first week</a></li>
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs002.html#___sec1" style="font-size: 80%;">Lectures and ComputerLab</a></li>
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers and ComputerLab</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
-     <!-- navigation toc: --> <li><a href="#___sec9" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
+     <!-- navigation toc: --> <li><a href="#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
 
         </ul>
       </li>
@@ -110,26 +112,22 @@ end of tocinfo -->
 <a name="part0010"></a>
 <!-- !split -->
 
-<h2 id="___sec9" class="anchor">Other courses on Data science and Machine Learning  at UiO </h2>
+<h2 id="___sec9" class="anchor">Extremely useful tools, strongly recommended </h2>
 
 <p>
-The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/" target="_self"><tt>https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/</tt></a>  gives an excellent overview of courses on Machine learning at UiO.
+<div class="panel panel-default">
+<div class="panel-body">
+<p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
 
-<ol>
-<li> <a href="http://www.uio.no/studier/emner/matnat/math/STK2100/index-eng.html" target="_self">STK2100 Machine learning and statistical methods for prediction and classification</a>.</li> 
-<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html" target="_self">IN3050 Introduction to Artificial Intelligence and Machine Learning</a>. Introductory course in machine learning and AI with an algorithmic approach.</li> 
-<li> <a href="http://www.uio.no/studier/emner/matnat/math/STK-INF3000/index-eng.html" target="_self">STK-INF3000/4000 Selected Topics in Data Science</a>. The course provides insight into selected contemporary relevant topics within Data Science.</li> 
-<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN4080/index.html" target="_self">IN4080 Natural Language Processing</a>. Probabilistic and machine learning techniques applied to natural language processing.</li> 
-<li> <a href="https://www.uio.no/studier/emner/matnat/math/STK-IN4300/index-eng.html" target="_self">STK-IN4300 Statistical learning methods in Data Science</a>. An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.</li>
-<li> <a href="http://www.uio.no/studier/emner/matnat/ifi/INF4490/" target="_self">INF4490 Biologically Inspired Computing</a>. An introduction to self-adapting methods also called artificial intelligence or machine learning.</li> 
-<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN-STK5000/index-eng.html" target="_self">IN-STK5000  Adaptive Methods for Data-Based Decision Making</a>. Methods for adaptive collection and processing of data based on machine learning techniques.</li> 
-<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN5400/" target="_self">IN5400/INF5860 Machine Learning for Image Analysis</a>. An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.</li>
-<li> <a href="https://www.uio.no/studier/emner/matnat/its/TEK5040/" target="_self">TEK5040 Deep learning for autonomous systems</a>. The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.</li>
-<li> <a href="https://www.uio.no/studier/emner/matnat/math/STK4051/index-eng.html" target="_self">STK4051 Computational Statistics</a></li>
-<li> <a href="https://www.uio.no/studier/emner/matnat/math/STK4021/index-eng.html" target="_self">STK4021 Applied Bayesian Analysis and Numerical Methods</a></li>
-</ol>
+<ul>
+  <li> GIT for version control, and GitHub or GitLab as repositories, highly recommended. This will be discussed during the first exercise session</li>
+  <li> Anaconda and other Python environments, see intro slides and first exercise session</li>
+</ul>
+</div>
+</div>
 
 
+<p>
 <p>
 <!-- navigation buttons at the bottom of the page -->
 <ul class="pagination">
@@ -145,6 +143,8 @@ The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus
   <li><a href="._Intro2Course-bs008.html">9</a></li>
   <li><a href="._Intro2Course-bs009.html">10</a></li>
   <li class="active"><a href="._Intro2Course-bs010.html">11</a></li>
+  <li><a href="._Intro2Course-bs011.html">12</a></li>
+  <li><a href="._Intro2Course-bs011.html">&raquo;</a></li>
 </ul>
 <!-- ------------------- end of main content --------------- -->
 
diff --git a/doc/pub/Intro2Course/html/._Intro2Course-bs011.html b/doc/pub/Intro2Course/html/._Intro2Course-bs011.html
new file mode 100644
index 000000000..b45de4df1
--- /dev/null
+++ b/doc/pub/Intro2Course/html/._Intro2Course-bs011.html
@@ -0,0 +1,173 @@
+<!--
+Automatically generated HTML file from DocOnce source
+(https://github.com/hplgit/doconce/)
+-->
+<html>
+<head>
+<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="Applied Data Analysis and Machine Learning: Introduction to the course, Logistics and Practicalities">
+
+<title>Applied Data Analysis and Machine Learning: Introduction to the course, Logistics and Practicalities</title>
+
+<!-- Bootstrap style: bootstrap -->
+<link href="https://netdna.bootstrapcdn.com/bootstrap/3.1.1/css/bootstrap.min.css" rel="stylesheet">
+<!-- not necessary
+<link href="https://netdna.bootstrapcdn.com/font-awesome/4.0.3/css/font-awesome.css" rel="stylesheet">
+-->
+
+<style type="text/css">
+
+/* Add scrollbar to dropdown menus in bootstrap navigation bar */
+.dropdown-menu {
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+   bar */
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+  content:"";
+  display:block;
+  height:50px;      /* fixed header height for style bootstrap */
+  margin:-50px 0 0; /* negative fixed header height */
+}
+</style>
+
+
+</head>
+
+<!-- tocinfo
+{'highest level': 2,
+ 'sections': [('Overview of first week', 2, None, '___sec0'),
+              ('Lectures and ComputerLab', 2, None, '___sec1'),
+              ('Course Format', 2, None, '___sec2'),
+              ('Teachers', 2, None, '___sec3'),
+              ('Deadlines for projects (tentative)', 2, None, '___sec4'),
+              ('Prerequisites', 2, None, '___sec5'),
+              ('Learning outcomes', 2, None, '___sec6'),
+              ('Topics covered in this course: Statistical analysis and '
+               'optimization of data',
+               2,
+               None,
+               '___sec7'),
+              ('Topics covered in this course: Machine Learning',
+               2,
+               None,
+               '___sec8'),
+              ('Extremely useful tools, strongly recommended',
+               2,
+               None,
+               '___sec9'),
+              ('Other courses on Data science and Machine Learning  at UiO',
+               2,
+               None,
+               '___sec10')]}
+end of tocinfo -->
+
+<body>
+
+    
+<!-- Bootstrap navigation bar -->
+<div class="navbar navbar-default navbar-fixed-top">
+  <div class="navbar-header">
+    <button type="button" class="navbar-toggle" data-toggle="collapse" data-target=".navbar-responsive-collapse">
+      <span class="icon-bar"></span>
+      <span class="icon-bar"></span>
+      <span class="icon-bar"></span>
+    </button>
+    <a class="navbar-brand" href="Intro2Course-bs.html">Applied Data Analysis and Machine Learning: Introduction to the course, Logistics and Practicalities</a>
+  </div>
+
+  <div class="navbar-collapse collapse navbar-responsive-collapse">
+    <ul class="nav navbar-nav navbar-right">
+      <li class="dropdown">
+        <a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
+        <ul class="dropdown-menu">
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs001.html#___sec0" style="font-size: 80%;">Overview of first week</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs002.html#___sec1" style="font-size: 80%;">Lectures and ComputerLab</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
+     <!-- navigation toc: --> <li><a href="#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
+
+        </ul>
+      </li>
+    </ul>
+  </div>
+</div>
+</div> <!-- end of navigation bar -->
+
+<div class="container">
+
+<p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p> <!-- add vertical space -->
+
+<a name="part0011"></a>
+<!-- !split -->
+
+<h2 id="___sec10" class="anchor">Other courses on Data science and Machine Learning  at UiO </h2>
+
+<p>
+The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/" target="_self"><tt>https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/</tt></a>  gives an excellent overview of courses on Machine learning at UiO.
+
+<ol>
+<li> <a href="http://www.uio.no/studier/emner/matnat/math/STK2100/index-eng.html" target="_self">STK2100 Machine learning and statistical methods for prediction and classification</a>.</li> 
+<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html" target="_self">IN3050 Introduction to Artificial Intelligence and Machine Learning</a>. Introductory course in machine learning and AI with an algorithmic approach.</li> 
+<li> <a href="http://www.uio.no/studier/emner/matnat/math/STK-INF3000/index-eng.html" target="_self">STK-INF3000/4000 Selected Topics in Data Science</a>. The course provides insight into selected contemporary relevant topics within Data Science.</li> 
+<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN4080/index.html" target="_self">IN4080 Natural Language Processing</a>. Probabilistic and machine learning techniques applied to natural language processing.</li> 
+<li> <a href="https://www.uio.no/studier/emner/matnat/math/STK-IN4300/index-eng.html" target="_self">STK-IN4300 Statistical learning methods in Data Science</a>. An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.</li>
+<li> <a href="http://www.uio.no/studier/emner/matnat/ifi/INF4490/" target="_self">INF4490 Biologically Inspired Computing</a>. An introduction to self-adapting methods also called artificial intelligence or machine learning.</li> 
+<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN-STK5000/index-eng.html" target="_self">IN-STK5000  Adaptive Methods for Data-Based Decision Making</a>. Methods for adaptive collection and processing of data based on machine learning techniques.</li> 
+<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN5400/" target="_self">IN5400/INF5860 Machine Learning for Image Analysis</a>. An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.</li>
+<li> <a href="https://www.uio.no/studier/emner/matnat/its/TEK5040/" target="_self">TEK5040 Deep learning for autonomous systems</a>. The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.</li>
+<li> <a href="https://www.uio.no/studier/emner/matnat/math/STK4051/index-eng.html" target="_self">STK4051 Computational Statistics</a></li>
+<li> <a href="https://www.uio.no/studier/emner/matnat/math/STK4021/index-eng.html" target="_self">STK4021 Applied Bayesian Analysis and Numerical Methods</a></li>
+</ol>
+
+
+<p>
+<!-- navigation buttons at the bottom of the page -->
+<ul class="pagination">
+<li><a href="._Intro2Course-bs010.html">&laquo;</a></li>
+  <li><a href="._Intro2Course-bs000.html">1</a></li>
+  <li><a href="">...</a></li>
+  <li><a href="._Intro2Course-bs003.html">4</a></li>
+  <li><a href="._Intro2Course-bs004.html">5</a></li>
+  <li><a href="._Intro2Course-bs005.html">6</a></li>
+  <li><a href="._Intro2Course-bs006.html">7</a></li>
+  <li><a href="._Intro2Course-bs007.html">8</a></li>
+  <li><a href="._Intro2Course-bs008.html">9</a></li>
+  <li><a href="._Intro2Course-bs009.html">10</a></li>
+  <li><a href="._Intro2Course-bs010.html">11</a></li>
+  <li class="active"><a href="._Intro2Course-bs011.html">12</a></li>
+</ul>
+<!-- ------------------- end of main content --------------- -->
+
+</div>  <!-- end container -->
+<!-- include javascript, jQuery *first* -->
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+<script src="https://netdna.bootstrapcdn.com/bootstrap/3.0.0/js/bootstrap.min.js"></script>
+
+<!-- Bootstrap footer
+<footer>
+<a href="http://..."><img width="250" align=right src="http://..."></a>
+</footer>
+-->
+
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+<center style="font-size:80%">
+<!-- copyright only on the titlepage -->
+</center>
+
+
+</body>
+</html>
+    
+
diff --git a/doc/pub/Intro2Course/html/Intro2Course-bs.html b/doc/pub/Intro2Course/html/Intro2Course-bs.html
index 01b5f208a..c788d6e82 100644
--- a/doc/pub/Intro2Course/html/Intro2Course-bs.html
+++ b/doc/pub/Intro2Course/html/Intro2Course-bs.html
@@ -44,26 +44,27 @@ Automatically generated HTML file from DocOnce source
  'sections': [('Overview of first week', 2, None, '___sec0'),
               ('Lectures and ComputerLab', 2, None, '___sec1'),
               ('Course Format', 2, None, '___sec2'),
-              ('Teachers and ComputerLab', 2, None, '___sec3'),
+              ('Teachers', 2, None, '___sec3'),
               ('Deadlines for projects (tentative)', 2, None, '___sec4'),
-              ('Learning outcomes', 2, None, '___sec5'),
+              ('Prerequisites', 2, None, '___sec5'),
+              ('Learning outcomes', 2, None, '___sec6'),
               ('Topics covered in this course: Statistical analysis and '
                'optimization of data',
                2,
                None,
-               '___sec6'),
+               '___sec7'),
               ('Topics covered in this course: Machine Learning',
                2,
                None,
-               '___sec7'),
+               '___sec8'),
               ('Extremely useful tools, strongly recommended',
                2,
                None,
-               '___sec8'),
+               '___sec9'),
               ('Other courses on Data science and Machine Learning  at UiO',
                2,
                None,
-               '___sec9')]}
+               '___sec10')]}
 end of tocinfo -->
 
 <body>
@@ -88,13 +89,14 @@ end of tocinfo -->
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs001.html#___sec0" style="font-size: 80%;">Overview of first week</a></li>
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs002.html#___sec1" style="font-size: 80%;">Lectures and ComputerLab</a></li>
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers and ComputerLab</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
      <!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
-     <!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
+     <!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning  at UiO</a></li>
 
         </ul>
       </li>
@@ -129,7 +131,7 @@ end of tocinfo -->
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Nov 12, 2019</h4></center> <!-- date -->
+<center><h4>Aug 19, 2020</h4></center> <!-- date -->
 <br>
 <p>
 
@@ -153,7 +155,7 @@ end of tocinfo -->
   <li><a href="._Intro2Course-bs008.html">9</a></li>
   <li><a href="._Intro2Course-bs009.html">10</a></li>
   <li><a href="">...</a></li>
-  <li><a href="._Intro2Course-bs010.html">11</a></li>
+  <li><a href="._Intro2Course-bs011.html">12</a></li>
   <li><a href="._Intro2Course-bs001.html">&raquo;</a></li>
 </ul>
 <!-- ------------------- end of main content --------------- -->
@@ -171,7 +173,7 @@ end of tocinfo -->
 
 
 <center style="font-size:80%">
-<!-- copyright --> &copy; 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
+<!-- copyright --> &copy; 1999-2020, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
 </center>
 
 
diff --git a/doc/pub/Intro2Course/html/Intro2Course-reveal.html b/doc/pub/Intro2Course/html/Intro2Course-reveal.html
index d49c2fc9b..ee51596a4 100644
--- a/doc/pub/Intro2Course/html/Intro2Course-reveal.html
+++ b/doc/pub/Intro2Course/html/Intro2Course-reveal.html
@@ -132,12 +132,12 @@ td.padding {
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>&nbsp;<br>
-<center><h4>Nov 12, 2019</h4></center> <!-- date -->
+<center><h4>Aug 19, 2020</h4></center> <!-- date -->
 <br>
 <p>
 
 <center style="font-size:80%">
-<!-- copyright --> &copy; 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
+<!-- copyright --> &copy; 1999-2020, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
 </center>
 </section>
 
@@ -150,13 +150,13 @@ td.padding {
 <b></b>
 <ul>
 
-<p><li> Thursday August 22: First lecture: Presentation of the course, aims and content</li>
+<p><li> Thursday August 20: First lecture: Presentation of the course, aims and content</li>
 
-<p><li> Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra</li>
+<p><li> Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra and elements of statistics</li>
 
-<p><li> Friday August 23: Linear regression</li>
+<p><li> Friday August 21: Linear regression</li>
 
-<p><li> Computer lab: Tuesday. First time: Tuesday August 27.</li> 
+<p><li> Computer lab: Wednesdays, 8am-6pm. First time: Wednesday August 26.</li> 
 </ul>
 </div>
 </section>
@@ -170,18 +170,14 @@ td.padding {
 <b></b>
 <ul>
 
-<p><li> Lectures: Thursday (2.15pm-4pm, this may change) and Friday (12.15pm-2pm).</li>
+<p><li> Lectures: Thursday (12.15pm-2pm  and Friday (12.15pm-2pm).  Due to the present COVID-19 situation all lectures will be online. They will be recorded and posted online at the official UiO <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/index.html" target="_blank">website</a>.</li>
 
-<p><li> Weekly reading assignments needed to solve projects and exercises.</li>
+<p><li> Weekly reading assignments and videos needed to solve projects and exercises.</li>
 
 <p><li> Weekly exercises when not working on projects. You can hand in exercises if you want.</li>
 
-<p><li> First hour of each lab session may be used to discuss technicalities, address questions etc linked with projects and exercises.</li>
-
 <p><li> Detailed lecture notes, exercises, all programs presented, projects etc can be found at the homepage of the course.</li>
 
-<p><li> Computerlab: Tuesday (8am-4pm), VB IT-auditorium 3. Depending on how many enlist we may extend the lab sessions</li>
-
 <p><li> Weekly plans and all other information are on the official webpage.</li>
 
 <p><li> No final exam, three projects that are graded and have to be approved.</li> 
@@ -219,7 +215,7 @@ td.padding {
 
 
 <section>
-<h2 id="___sec3">Teachers and ComputerLab </h2>
+<h2 id="___sec3">Teachers  </h2>
 
 <p>
 <div class="alert alert-block alert-block alert-text-normal">
@@ -227,27 +223,28 @@ td.padding {
 <p>
 <b>Teachers :</b>
 
-<ol>
-<p><li> <a href="https://www.researchgate.net/profile/Hanna_Svennevik" target="_blank">Hanna Svennevik</a></li>
-<p><li> <a href="http://mhjgit.github.io/info/doc/web/" target="_blank">Morten Hjorth-Jensen</a></li>
-<p><li> <a href="https://no.linkedin.com/in/lucas-charpentier-176206171" target="_blank">Lucas Charpentier</a></li>
-<p><li> <a href="https://www.researchgate.net/profile/Stian_Bilek" target="_blank">Stian Bilek</a></li>
-<p><li> <a href="https://github.com/Schoyen" target="_blank">&#216;yvind Sigmundson Sch&#248;yen</a></li>
-</ol>
-<p>
+<ul>
+<p><li> Morten Hjorth-Jensen, morten.hjorth-jensen@fys.uio.no</li>
 
-<table border="1">
-<thead>
-<tr><th align="center">      day       </th> <th align="center">   Time  </th> </tr>
-</thead>
-<tbody>
-<tr><td align="center">   Group 1: Tuesday    </td> <td align="center">   8am-10am     </td> </tr>
-<tr><td align="center">   Group 2: Tuesday    </td> <td align="center">   10am-12pm    </td> </tr>
-<tr><td align="center">   Group 3: Tuesday    </td> <td align="center">   12pm-2pm     </td> </tr>
-<tr><td align="center">   Group 4: Tuesday    </td> <td align="center">   2pm-4pm      </td> </tr>
-</tbody>
-</table>
+<ul>
 
+<p><li> <b>Phone</b>: +47-48257387</li>
+
+<p><li> <b>Office</b>: Department of Physics, University of Oslo, Eastern wing, room F&#216;470</li>
+
+<p><li> <b>Office hours</b>: <em>Anytime</em>! In Fall Semester 2020 (FS20), as a rule of thumb office hours are planned via computer or telephone. Individual or group office hours will be performed via zoom. Feel free to send an email for planning. In person meetings may also be possible if allowed by the University of Oslo's COVID-19 instructions.</li>
+</ul>
+<p><li> &#216;yvind Sigmundson Sch&#248;yen, oyvinssc@student.matnat.uio.no</li>
+
+<ul>
+
+<p><li> <b>Office</b>: Department of Physics, University of Oslo, Eastern wing, room F&#216;452</li>
+</ul>
+<p><li> Michael Bitney, m.s.bitney@fys.uio.no</li>
+<p><li> Kristian Wold, kriswold@student.matnat.uio.no</li>
+<p><li> Nicolai Haug, nicoha@student.matnat.uio.no</li>
+<p><li> Per-Dimitri S&#248;nsteland, perdimitri.bs@gmail.com</li>
+</ul>
 </div>
 </section>
 
@@ -259,13 +256,13 @@ td.padding {
 <div class="alert alert-block alert-block alert-text-normal">
 <b></b>
 <ol>
-<p><li> Project 1: September 30 (graded with feedback)</li>
-<p><li> Project 2: November 13 (graded with feedback)</li>
-<p><li> Project 3: December 15  (graded with feedback)</li>
+<p><li> Project 1: September 28 (graded with feedback)</li>
+<p><li> Project 2: November 2 (graded with feedback)</li>
+<p><li> Project 3: December 7  (graded with feedback)</li>
 </ol>
 <p>
 
-Projects are handed in using devilry.ifi.uio.no. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via devilry.
+Projects are handed in using <b>Canvas</b>. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via <b>Canvas</b>.
 
 
 </div>
@@ -273,49 +270,81 @@ Projects are handed in using devilry.ifi.uio.no. We use Github as repository for
 
 
 <section>
-<h2 id="___sec5">Learning outcomes </h2>
+<h2 id="___sec5">Prerequisites </h2>
+
+<p>
+Basic knowledge in programming and mathematics, with an emphasis on
+linear algebra. Knowledge of Python or/and C++ as programming
+languages is strongly recommended and experience with Jupiter notebook
+is recommended. Required courses are the equivalents to the University
+of Oslo mathematics courses MAT1100, MAT1110, MAT1120 and at least one
+of the corresponding computing and programming courses INF1000/INF1110
+or MAT-INF1100/MAT-INF1100L/BIOS1100/KJM-INF1100. Most universities
+offer nowadays a basic programming course (often compulsory) where
+Python is the recurring programming language.
+</section>
+
+
+<section>
+<h2 id="___sec6">Learning outcomes </h2>
 
 <p>
 <div class="alert alert-block alert-block alert-text-normal">
 <b></b>
+<p>
+This course aims at giving you insights and knowledge about  many of the central algorithms used in Data Analysis and Machine Learning.  The course is project based and through  various numerical projects, normally three, you will be exposed to fundamental research problems in these fields, with the aim to reproduce state of the art scientific results. Both supervised and unsupervised methods will be covered. The emphasis is on a frequentist approach, although we will try to link it with a Bayesian approach as well. You will learn to develop and structure large codes for studying different cases where Machine Learning is applied to, get acquainted with computing facilities and learn to handle large scientific projects. A good scientific and ethical conduct is emphasized throughout the course. More specifically, after this course you will
+
 <ul>
-<p><li> Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, data optimization and machine learning</li>
-<p><li> Be capable of extending the acquired knowledge to other systems and cases</li>
-<p><li> Have an understanding of central algorithms used in data analysis and machine learning</li>
-<p><li> Gain knowledge of central aspects of Monte Carlo methods, Markov chains, Gibbs samplers and their possible applications</li>
-<p><li> Understand linear methods for regression and classification, from ordinary least squares, via Lasso and Ridge to Logistic regression</li>
-<p><li> Learn about various neural networks and deep  learning methods for supervised and unsupervised learning</li>
-<p><li> Learn about about decision trees and random forests</li>
-<p><li> Learn about support vector machines and kernel transformations</li>
-<p><li> Reduction of data sets, from PCA to clustering, supervised and unsupervided methods</li>
-<p><li> Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++</li>
+<p><li> Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, data optimization and machine learning;</li>
+<p><li> Be capable of extending the acquired knowledge to other systems and cases;</li>
+<p><li> Have an understanding of central algorithms used in data analysis and machine learning;</li>
+<p><li> Understand linear methods for regression and classification, from ordinary least squares, via Lasso and Ridge to Logistic regression;</li>
+<p><li> Learn about neural networks and deep  learning methods for supervised and unsupervised learning. Emphasis on feed forward neural networks, convolutional and recurrent neural networks;</li> 
+<p><li> Learn about about decision trees, random forests, bagging and boosting methods;</li>
+<p><li> Learn about support vector machines and kernel transformations;</li>
+<p><li> Reduction of data sets, from PCA to clustering;</li>
+<p><li> Autoencoders and Reinforcement Learning;</li>
+<p><li> Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++ and/or Fortran (Fortran2003 or later).</li>  
 </ul>
 </div>
 </section>
 
 
 <section>
-<h2 id="___sec6">Topics covered in this course: Statistical analysis and optimization of data </h2>
+<h2 id="___sec7">Topics covered in this course: Statistical analysis and optimization of data </h2>
+
+<p>
+The course has two central parts
+
+<ol>
+<p><li> Statistical analysis and optimization of data</li>
+<p><li> Machine learning</li>
+</ol>
+<p>
+
+These topics will be scattered thorughout the course and may not  necessarily be taught separately. Rather, we will often take an approach (during the lectures and project/exercise sessions) where say elements from statistical data analysis are mixed with specific Machine Learning algorithms
 
 <p>
 <div class="alert alert-block alert-block alert-text-normal">
-<b></b>
+<b>Statistical analysis and optimization of data.</b>
+<p>
+The following topics will be covered
+
 <ul>
-<p><li> Basic concepts, expectation values, variance, covariance, correlation functions and errors</li>
-<p><li> Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions</li>
-<p><li> Central elements of Bayesian statistics and modeling</li>
-<p><li> Gradient methods for data optimization</li>
-<p><li> Monte Carlo methods, Markov chains, Metropolis-Hastings algorithm</li>
-<p><li> Linear methods for regression and classification</li>
-<p><li> Estimation of errors using cross-validation, blocking, bootstrapping and jackknife methods</li>
-<p><li> Practical optimization using Singular-value decomposition and least squares for parameterizing data</li>
+<p><li> Basic concepts, expectation values, variance, covariance, correlation functions and errors;</li>
+<p><li> Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;</li>
+<p><li> Central elements of Bayesian statistics and modeling;</li>
+<p><li> Gradient methods for data optimization,</li> 
+<p><li> Monte Carlo methods, Markov chains, Gibbs sampling and Metropolis-Hastings sampling;</li>
+<p><li> Estimation of errors and resampling techniques such as the cross-validation, blocking, bootstrapping and jackknife methods;</li>
+<p><li> Principal Component Analysis (PCA) and its mathematical foundation</li>
 </ul>
 </div>
 </section>
 
 
 <section>
-<h2 id="___sec7">Topics covered in this course: Machine Learning </h2>
+<h2 id="___sec8">Topics covered in this course: Machine Learning </h2>
 
 <p>
 <div class="alert alert-block alert-block alert-text-normal">
@@ -324,38 +353,41 @@ Projects are handed in using devilry.ifi.uio.no. We use Github as repository for
 The following topics will be covered
 
 <ul>
-<p><li> Linear Regression and Logistic Regression</li>
-<p><li> Neural networks and deep learning</li>
-<p><li> Decisions trees and nearest neighbor algorithms</li>
+<p><li> Linear Regression and Logistic Regression;</li>
+<p><li> Neural networks and deep learning, including convolutional and recurrent neural networks</li>
+<p><li> Decisions trees, Random Forests, Bagging and Boosting</li>
 <p><li> Support vector machines</li>
-<p><li> Bayesian Neural Networks</li>
+<p><li> Bayesian linear and logistic regression</li>
 <p><li> Boltzmann Machines</li>
-<p><li> Dimensionality reduction, from PCA to cluster models</li>
+<p><li> Unsupervised learning Dimensionality reduction, from PCA to cluster models</li>
 </ul>
+<p>
+
+Hands-on demonstrations, exercises and projects aim at deepening your understanding of these topics.
+
+
 </div>
 </section>
 
 
 <section>
-<h2 id="___sec8">Extremely useful tools, strongly recommended </h2>
+<h2 id="___sec9">Extremely useful tools, strongly recommended </h2>
 
 <p>
 <div class="alert alert-block alert-block alert-text-normal">
 <b>and discussed at the lab sessions.</b>
 <ul>
 
-<p><li> GIT for version control, highly recommended</li>
+<p><li> GIT for version control, and GitHub or GitLab as repositories, highly recommended. This will be discussed during the first exercise session</li>
 
-<p><li> Devilry for handing in projects, next week</li>
-
-<p><li> Anaconda and other Python environments, see intro slides</li>
+<p><li> Anaconda and other Python environments, see intro slides and first exercise session</li>
 </ul>
 </div>
 </section>
 
 
 <section>
-<h2 id="___sec9">Other courses on Data science and Machine Learning  at UiO </h2>
+<h2 id="___sec10">Other courses on Data science and Machine Learning  at UiO </h2>
 
 <p>
 The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/" target="_blank"><tt>https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/</tt></a>  gives an excellent overview of courses on Machine learning at UiO.
diff --git a/doc/pub/Intro2Course/html/Intro2Course-solarized.html b/doc/pub/Intro2Course/html/Intro2Course-solarized.html
index 3a0a03acd..b6de77c52 100644
--- a/doc/pub/Intro2Course/html/Intro2Course-solarized.html
+++ b/doc/pub/Intro2Course/html/Intro2Course-solarized.html
@@ -64,26 +64,27 @@ div { text-align: justify; text-justify: inter-word; }
  'sections': [('Overview of first week', 2, None, '___sec0'),
               ('Lectures and ComputerLab', 2, None, '___sec1'),
               ('Course Format', 2, None, '___sec2'),
-              ('Teachers and ComputerLab', 2, None, '___sec3'),
+              ('Teachers', 2, None, '___sec3'),
               ('Deadlines for projects (tentative)', 2, None, '___sec4'),
-              ('Learning outcomes', 2, None, '___sec5'),
+              ('Prerequisites', 2, None, '___sec5'),
+              ('Learning outcomes', 2, None, '___sec6'),
               ('Topics covered in this course: Statistical analysis and '
                'optimization of data',
                2,
                None,
-               '___sec6'),
+               '___sec7'),
               ('Topics covered in this course: Machine Learning',
                2,
                None,
-               '___sec7'),
+               '___sec8'),
               ('Extremely useful tools, strongly recommended',
                2,
                None,
-               '___sec8'),
+               '___sec9'),
               ('Other courses on Data science and Machine Learning  at UiO',
                2,
                None,
-               '___sec9')]}
+               '___sec10')]}
 end of tocinfo -->
 
 <body>
@@ -109,7 +110,7 @@ end of tocinfo -->
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Nov 12, 2019</h4></center> <!-- date -->
+<center><h4>Aug 19, 2020</h4></center> <!-- date -->
 <br>
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -122,10 +123,10 @@ end of tocinfo -->
 <p>
 
 <ul>
-  <li> Thursday August 22: First lecture: Presentation of the course, aims and content</li>
-  <li> Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra</li>
-  <li> Friday August 23: Linear regression</li>   
-  <li> Computer lab: Tuesday. First time: Tuesday August 27.</li> 
+  <li> Thursday August 20: First lecture: Presentation of the course, aims and content</li>
+  <li> Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra and elements of statistics</li>
+  <li> Friday August 21: Linear regression</li>   
+  <li> Computer lab: Wednesdays, 8am-6pm. First time: Wednesday August 26.</li> 
 </ul>
 </div>
 
@@ -141,12 +142,10 @@ end of tocinfo -->
 <p>
 
 <ul>
-  <li> Lectures: Thursday (2.15pm-4pm, this may change) and Friday (12.15pm-2pm).</li>
-  <li> Weekly reading assignments needed to solve projects and exercises.</li>
+  <li> Lectures: Thursday (12.15pm-2pm  and Friday (12.15pm-2pm).  Due to the present COVID-19 situation all lectures will be online. They will be recorded and posted online at the official UiO <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/index.html" target="_blank">website</a>.</li>
+  <li> Weekly reading assignments and videos needed to solve projects and exercises.</li>
   <li> Weekly exercises when not working on projects. You can hand in exercises if you want.</li>
-  <li> First hour of each lab session may be used to discuss technicalities, address questions etc linked with projects and exercises.</li>
   <li> Detailed lecture notes, exercises, all programs presented, projects etc can be found at the homepage of the course.</li>
-  <li> Computerlab: Tuesday (8am-4pm), VB IT-auditorium 3. Depending on how many enlist we may extend the lab sessions</li>
   <li> Weekly plans and all other information are on the official webpage.</li>
   <li> No final exam, three projects that are graded and have to be approved.</li> 
 </ul>
@@ -182,7 +181,7 @@ end of tocinfo -->
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 
-<h2 id="___sec3">Teachers and ComputerLab </h2>
+<h2 id="___sec3">Teachers  </h2>
 
 <p>
 <div class="alert alert-block alert-block alert-text-normal">
@@ -192,26 +191,26 @@ end of tocinfo -->
 <p>
 <b>Teachers :</b>
 
-<ol>
-<li> <a href="https://www.researchgate.net/profile/Hanna_Svennevik" target="_blank">Hanna Svennevik</a></li>
-<li> <a href="http://mhjgit.github.io/info/doc/web/" target="_blank">Morten Hjorth-Jensen</a></li>
-<li> <a href="https://no.linkedin.com/in/lucas-charpentier-176206171" target="_blank">Lucas Charpentier</a></li>
-<li> <a href="https://www.researchgate.net/profile/Stian_Bilek" target="_blank">Stian Bilek</a></li>
-<li> <a href="https://github.com/Schoyen" target="_blank">&#216;yvind Sigmundson Sch&#248;yen</a></li>
-</ol>
+<ul>
+<li> Morten Hjorth-Jensen, morten.hjorth-jensen@fys.uio.no</li>
 
-<table border="1">
-<thead>
-<tr><th align="center">      day       </th> <th align="center">   Time  </th> </tr>
-</thead>
-<tbody>
-<tr><td align="center">   Group 1: Tuesday    </td> <td align="center">   8am-10am     </td> </tr>
-<tr><td align="center">   Group 2: Tuesday    </td> <td align="center">   10am-12pm    </td> </tr>
-<tr><td align="center">   Group 3: Tuesday    </td> <td align="center">   12pm-2pm     </td> </tr>
-<tr><td align="center">   Group 4: Tuesday    </td> <td align="center">   2pm-4pm      </td> </tr>
-</tbody>
-</table>
+<ul>
+  <li> <b>Phone</b>: +47-48257387</li>
+  <li> <b>Office</b>: Department of Physics, University of Oslo, Eastern wing, room F&#216;470</li> 
+  <li> <b>Office hours</b>: <em>Anytime</em>! In Fall Semester 2020 (FS20), as a rule of thumb office hours are planned via computer or telephone. Individual or group office hours will be performed via zoom. Feel free to send an email for planning. In person meetings may also be possible if allowed by the University of Oslo's COVID-19 instructions.</li>
+</ul>
 
+<li> &#216;yvind Sigmundson Sch&#248;yen, oyvinssc@student.matnat.uio.no</li>
+
+<ul>
+  <li> <b>Office</b>: Department of Physics, University of Oslo, Eastern wing, room F&#216;452</li>
+</ul>
+
+<li> Michael Bitney, m.s.bitney@fys.uio.no</li>
+<li> Kristian Wold, kriswold@student.matnat.uio.no</li>
+<li> Nicolai Haug, nicoha@student.matnat.uio.no</li>
+<li> Per-Dimitri S&#248;nsteland, perdimitri.bs@gmail.com</li>
+</ul>
 </div>
 
 
@@ -226,12 +225,12 @@ end of tocinfo -->
 <p>
 
 <ol>
-<li> Project 1: September 30 (graded with feedback)</li>
-<li> Project 2: November 13 (graded with feedback)</li>
-<li> Project 3: December 15  (graded with feedback)</li>
+<li> Project 1: September 28 (graded with feedback)</li>
+<li> Project 2: November 2 (graded with feedback)</li>
+<li> Project 3: December 7  (graded with feedback)</li>
 </ol>
 
-Projects are handed in using devilry.ifi.uio.no. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via devilry.
+Projects are handed in using <b>Canvas</b>. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via <b>Canvas</b>.
 
 
 </div>
@@ -240,24 +239,43 @@ Projects are handed in using devilry.ifi.uio.no. We use Github as repository for
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 
-<h2 id="___sec5">Learning outcomes </h2>
+<h2 id="___sec5">Prerequisites </h2>
+
+<p>
+Basic knowledge in programming and mathematics, with an emphasis on
+linear algebra. Knowledge of Python or/and C++ as programming
+languages is strongly recommended and experience with Jupiter notebook
+is recommended. Required courses are the equivalents to the University
+of Oslo mathematics courses MAT1100, MAT1110, MAT1120 and at least one
+of the corresponding computing and programming courses INF1000/INF1110
+or MAT-INF1100/MAT-INF1100L/BIOS1100/KJM-INF1100. Most universities
+offer nowadays a basic programming course (often compulsory) where
+Python is the recurring programming language.
+
+<p>
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+
+<h2 id="___sec6">Learning outcomes </h2>
 
 <p>
 <div class="alert alert-block alert-block alert-text-normal">
 <b></b>
 <p>
 
+<p>
+This course aims at giving you insights and knowledge about  many of the central algorithms used in Data Analysis and Machine Learning.  The course is project based and through  various numerical projects, normally three, you will be exposed to fundamental research problems in these fields, with the aim to reproduce state of the art scientific results. Both supervised and unsupervised methods will be covered. The emphasis is on a frequentist approach, although we will try to link it with a Bayesian approach as well. You will learn to develop and structure large codes for studying different cases where Machine Learning is applied to, get acquainted with computing facilities and learn to handle large scientific projects. A good scientific and ethical conduct is emphasized throughout the course. More specifically, after this course you will
+
 <ul>
-<li> Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, data optimization and machine learning</li>
-<li> Be capable of extending the acquired knowledge to other systems and cases</li>
-<li> Have an understanding of central algorithms used in data analysis and machine learning</li>
-<li> Gain knowledge of central aspects of Monte Carlo methods, Markov chains, Gibbs samplers and their possible applications</li>
-<li> Understand linear methods for regression and classification, from ordinary least squares, via Lasso and Ridge to Logistic regression</li>
-<li> Learn about various neural networks and deep  learning methods for supervised and unsupervised learning</li>
-<li> Learn about about decision trees and random forests</li>
-<li> Learn about support vector machines and kernel transformations</li>
-<li> Reduction of data sets, from PCA to clustering, supervised and unsupervided methods</li>
-<li> Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++</li>
+<li> Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, data optimization and machine learning;</li>
+<li> Be capable of extending the acquired knowledge to other systems and cases;</li>
+<li> Have an understanding of central algorithms used in data analysis and machine learning;</li>
+<li> Understand linear methods for regression and classification, from ordinary least squares, via Lasso and Ridge to Logistic regression;</li>
+<li> Learn about neural networks and deep  learning methods for supervised and unsupervised learning. Emphasis on feed forward neural networks, convolutional and recurrent neural networks;</li> 
+<li> Learn about about decision trees, random forests, bagging and boosting methods;</li>
+<li> Learn about support vector machines and kernel transformations;</li>
+<li> Reduction of data sets, from PCA to clustering;</li>
+<li> Autoencoders and Reinforcement Learning;</li>
+<li> Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++ and/or Fortran (Fortran2003 or later).</li>  
 </ul>
 </div>
 
@@ -265,22 +283,34 @@ Projects are handed in using devilry.ifi.uio.no. We use Github as repository for
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 
-<h2 id="___sec6">Topics covered in this course: Statistical analysis and optimization of data </h2>
+<h2 id="___sec7">Topics covered in this course: Statistical analysis and optimization of data </h2>
+
+<p>
+The course has two central parts
+
+<ol>
+<li> Statistical analysis and optimization of data</li>
+<li> Machine learning</li>
+</ol>
+
+These topics will be scattered thorughout the course and may not  necessarily be taught separately. Rather, we will often take an approach (during the lectures and project/exercise sessions) where say elements from statistical data analysis are mixed with specific Machine Learning algorithms
 
 <p>
 <div class="alert alert-block alert-block alert-text-normal">
-<b></b>
+<b>Statistical analysis and optimization of data.</b>
 <p>
 
+<p>
+The following topics will be covered
+
 <ul>
-<li> Basic concepts, expectation values, variance, covariance, correlation functions and errors</li>
-<li> Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions</li>
-<li> Central elements of Bayesian statistics and modeling</li>
-<li> Gradient methods for data optimization</li>
-<li> Monte Carlo methods, Markov chains, Metropolis-Hastings algorithm</li>
-<li> Linear methods for regression and classification</li>
-<li> Estimation of errors using cross-validation, blocking, bootstrapping and jackknife methods</li>
-<li> Practical optimization using Singular-value decomposition and least squares for parameterizing data</li>
+<li> Basic concepts, expectation values, variance, covariance, correlation functions and errors;</li>
+<li> Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;</li>
+<li> Central elements of Bayesian statistics and modeling;</li>
+<li> Gradient methods for data optimization,</li> 
+<li> Monte Carlo methods, Markov chains, Gibbs sampling and Metropolis-Hastings sampling;</li>
+<li> Estimation of errors and resampling techniques such as the cross-validation, blocking, bootstrapping and jackknife methods;</li>
+<li> Principal Component Analysis (PCA) and its mathematical foundation</li>
 </ul>
 </div>
 
@@ -288,7 +318,7 @@ Projects are handed in using devilry.ifi.uio.no. We use Github as repository for
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 
-<h2 id="___sec7">Topics covered in this course: Machine Learning </h2>
+<h2 id="___sec8">Topics covered in this course: Machine Learning </h2>
 
 <p>
 <div class="alert alert-block alert-block alert-text-normal">
@@ -297,21 +327,25 @@ Projects are handed in using devilry.ifi.uio.no. We use Github as repository for
 The following topics will be covered
 
 <ul>
-<li> Linear Regression and Logistic Regression</li>
-<li> Neural networks and deep learning</li>
-<li> Decisions trees and nearest neighbor algorithms</li>
+<li> Linear Regression and Logistic Regression;</li>
+<li> Neural networks and deep learning, including convolutional and recurrent neural networks</li>
+<li> Decisions trees, Random Forests, Bagging and Boosting</li>
 <li> Support vector machines</li>
-<li> Bayesian Neural Networks</li>
+<li> Bayesian linear and logistic regression</li>
 <li> Boltzmann Machines</li>
-<li> Dimensionality reduction, from PCA to cluster models</li>
+<li> Unsupervised learning Dimensionality reduction, from PCA to cluster models</li>
 </ul>
+
+Hands-on demonstrations, exercises and projects aim at deepening your understanding of these topics.
+
+
 </div>
 
 
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 
-<h2 id="___sec8">Extremely useful tools, strongly recommended </h2>
+<h2 id="___sec9">Extremely useful tools, strongly recommended </h2>
 
 <p>
 <div class="alert alert-block alert-block alert-text-normal">
@@ -319,9 +353,8 @@ The following topics will be covered
 <p>
 
 <ul>
-  <li> GIT for version control, highly recommended</li>
-  <li> Devilry for handing in projects, next week</li>
-  <li> Anaconda and other Python environments, see intro slides</li>
+  <li> GIT for version control, and GitHub or GitLab as repositories, highly recommended. This will be discussed during the first exercise session</li>
+  <li> Anaconda and other Python environments, see intro slides and first exercise session</li>
 </ul>
 </div>
 
@@ -329,7 +362,7 @@ The following topics will be covered
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 
-<h2 id="___sec9">Other courses on Data science and Machine Learning  at UiO </h2>
+<h2 id="___sec10">Other courses on Data science and Machine Learning  at UiO </h2>
 
 <p>
 The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/" target="_blank"><tt>https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/</tt></a>  gives an excellent overview of courses on Machine learning at UiO.
@@ -353,7 +386,7 @@ The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus
 
 
 <center style="font-size:80%">
-<!-- copyright --> &copy; 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
+<!-- copyright --> &copy; 1999-2020, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
 </center>
 
 
diff --git a/doc/pub/Intro2Course/html/Intro2Course.html b/doc/pub/Intro2Course/html/Intro2Course.html
index b1020da98..17eda1fe2 100644
--- a/doc/pub/Intro2Course/html/Intro2Course.html
+++ b/doc/pub/Intro2Course/html/Intro2Course.html
@@ -69,26 +69,27 @@ div { text-align: justify; text-justify: inter-word; }
  'sections': [('Overview of first week', 2, None, '___sec0'),
               ('Lectures and ComputerLab', 2, None, '___sec1'),
               ('Course Format', 2, None, '___sec2'),
-              ('Teachers and ComputerLab', 2, None, '___sec3'),
+              ('Teachers', 2, None, '___sec3'),
               ('Deadlines for projects (tentative)', 2, None, '___sec4'),
-              ('Learning outcomes', 2, None, '___sec5'),
+              ('Prerequisites', 2, None, '___sec5'),
+              ('Learning outcomes', 2, None, '___sec6'),
               ('Topics covered in this course: Statistical analysis and '
                'optimization of data',
                2,
                None,
-               '___sec6'),
+               '___sec7'),
               ('Topics covered in this course: Machine Learning',
                2,
                None,
-               '___sec7'),
+               '___sec8'),
               ('Extremely useful tools, strongly recommended',
                2,
                None,
-               '___sec8'),
+               '___sec9'),
               ('Other courses on Data science and Machine Learning  at UiO',
                2,
                None,
-               '___sec9')]}
+               '___sec10')]}
 end of tocinfo -->
 
 <body>
@@ -114,7 +115,7 @@ end of tocinfo -->
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Nov 12, 2019</h4></center> <!-- date -->
+<center><h4>Aug 19, 2020</h4></center> <!-- date -->
 <br>
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -127,10 +128,10 @@ end of tocinfo -->
 <p>
 
 <ul>
-  <li> Thursday August 22: First lecture: Presentation of the course, aims and content</li>
-  <li> Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra</li>
-  <li> Friday August 23: Linear regression</li>   
-  <li> Computer lab: Tuesday. First time: Tuesday August 27.</li> 
+  <li> Thursday August 20: First lecture: Presentation of the course, aims and content</li>
+  <li> Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra and elements of statistics</li>
+  <li> Friday August 21: Linear regression</li>   
+  <li> Computer lab: Wednesdays, 8am-6pm. First time: Wednesday August 26.</li> 
 </ul>
 </div>
 
@@ -146,12 +147,10 @@ end of tocinfo -->
 <p>
 
 <ul>
-  <li> Lectures: Thursday (2.15pm-4pm, this may change) and Friday (12.15pm-2pm).</li>
-  <li> Weekly reading assignments needed to solve projects and exercises.</li>
+  <li> Lectures: Thursday (12.15pm-2pm  and Friday (12.15pm-2pm).  Due to the present COVID-19 situation all lectures will be online. They will be recorded and posted online at the official UiO <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/index.html" target="_blank">website</a>.</li>
+  <li> Weekly reading assignments and videos needed to solve projects and exercises.</li>
   <li> Weekly exercises when not working on projects. You can hand in exercises if you want.</li>
-  <li> First hour of each lab session may be used to discuss technicalities, address questions etc linked with projects and exercises.</li>
   <li> Detailed lecture notes, exercises, all programs presented, projects etc can be found at the homepage of the course.</li>
-  <li> Computerlab: Tuesday (8am-4pm), VB IT-auditorium 3. Depending on how many enlist we may extend the lab sessions</li>
   <li> Weekly plans and all other information are on the official webpage.</li>
   <li> No final exam, three projects that are graded and have to be approved.</li> 
 </ul>
@@ -187,7 +186,7 @@ end of tocinfo -->
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 
-<h2 id="___sec3">Teachers and ComputerLab </h2>
+<h2 id="___sec3">Teachers  </h2>
 
 <p>
 <div class="alert alert-block alert-block alert-text-normal">
@@ -197,26 +196,26 @@ end of tocinfo -->
 <p>
 <b>Teachers :</b>
 
-<ol>
-<li> <a href="https://www.researchgate.net/profile/Hanna_Svennevik" target="_blank">Hanna Svennevik</a></li>
-<li> <a href="http://mhjgit.github.io/info/doc/web/" target="_blank">Morten Hjorth-Jensen</a></li>
-<li> <a href="https://no.linkedin.com/in/lucas-charpentier-176206171" target="_blank">Lucas Charpentier</a></li>
-<li> <a href="https://www.researchgate.net/profile/Stian_Bilek" target="_blank">Stian Bilek</a></li>
-<li> <a href="https://github.com/Schoyen" target="_blank">&#216;yvind Sigmundson Sch&#248;yen</a></li>
-</ol>
+<ul>
+<li> Morten Hjorth-Jensen, morten.hjorth-jensen@fys.uio.no</li>
 
-<table border="1">
-<thead>
-<tr><th align="center">      day       </th> <th align="center">   Time  </th> </tr>
-</thead>
-<tbody>
-<tr><td align="center">   Group 1: Tuesday    </td> <td align="center">   8am-10am     </td> </tr>
-<tr><td align="center">   Group 2: Tuesday    </td> <td align="center">   10am-12pm    </td> </tr>
-<tr><td align="center">   Group 3: Tuesday    </td> <td align="center">   12pm-2pm     </td> </tr>
-<tr><td align="center">   Group 4: Tuesday    </td> <td align="center">   2pm-4pm      </td> </tr>
-</tbody>
-</table>
+<ul>
+  <li> <b>Phone</b>: +47-48257387</li>
+  <li> <b>Office</b>: Department of Physics, University of Oslo, Eastern wing, room F&#216;470</li> 
+  <li> <b>Office hours</b>: <em>Anytime</em>! In Fall Semester 2020 (FS20), as a rule of thumb office hours are planned via computer or telephone. Individual or group office hours will be performed via zoom. Feel free to send an email for planning. In person meetings may also be possible if allowed by the University of Oslo's COVID-19 instructions.</li>
+</ul>
 
+<li> &#216;yvind Sigmundson Sch&#248;yen, oyvinssc@student.matnat.uio.no</li>
+
+<ul>
+  <li> <b>Office</b>: Department of Physics, University of Oslo, Eastern wing, room F&#216;452</li>
+</ul>
+
+<li> Michael Bitney, m.s.bitney@fys.uio.no</li>
+<li> Kristian Wold, kriswold@student.matnat.uio.no</li>
+<li> Nicolai Haug, nicoha@student.matnat.uio.no</li>
+<li> Per-Dimitri S&#248;nsteland, perdimitri.bs@gmail.com</li>
+</ul>
 </div>
 
 
@@ -231,12 +230,12 @@ end of tocinfo -->
 <p>
 
 <ol>
-<li> Project 1: September 30 (graded with feedback)</li>
-<li> Project 2: November 13 (graded with feedback)</li>
-<li> Project 3: December 15  (graded with feedback)</li>
+<li> Project 1: September 28 (graded with feedback)</li>
+<li> Project 2: November 2 (graded with feedback)</li>
+<li> Project 3: December 7  (graded with feedback)</li>
 </ol>
 
-Projects are handed in using devilry.ifi.uio.no. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via devilry.
+Projects are handed in using <b>Canvas</b>. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via <b>Canvas</b>.
 
 
 </div>
@@ -245,24 +244,43 @@ Projects are handed in using devilry.ifi.uio.no. We use Github as repository for
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 
-<h2 id="___sec5">Learning outcomes </h2>
+<h2 id="___sec5">Prerequisites </h2>
+
+<p>
+Basic knowledge in programming and mathematics, with an emphasis on
+linear algebra. Knowledge of Python or/and C++ as programming
+languages is strongly recommended and experience with Jupiter notebook
+is recommended. Required courses are the equivalents to the University
+of Oslo mathematics courses MAT1100, MAT1110, MAT1120 and at least one
+of the corresponding computing and programming courses INF1000/INF1110
+or MAT-INF1100/MAT-INF1100L/BIOS1100/KJM-INF1100. Most universities
+offer nowadays a basic programming course (often compulsory) where
+Python is the recurring programming language.
+
+<p>
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+
+<h2 id="___sec6">Learning outcomes </h2>
 
 <p>
 <div class="alert alert-block alert-block alert-text-normal">
 <b></b>
 <p>
 
+<p>
+This course aims at giving you insights and knowledge about  many of the central algorithms used in Data Analysis and Machine Learning.  The course is project based and through  various numerical projects, normally three, you will be exposed to fundamental research problems in these fields, with the aim to reproduce state of the art scientific results. Both supervised and unsupervised methods will be covered. The emphasis is on a frequentist approach, although we will try to link it with a Bayesian approach as well. You will learn to develop and structure large codes for studying different cases where Machine Learning is applied to, get acquainted with computing facilities and learn to handle large scientific projects. A good scientific and ethical conduct is emphasized throughout the course. More specifically, after this course you will
+
 <ul>
-<li> Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, data optimization and machine learning</li>
-<li> Be capable of extending the acquired knowledge to other systems and cases</li>
-<li> Have an understanding of central algorithms used in data analysis and machine learning</li>
-<li> Gain knowledge of central aspects of Monte Carlo methods, Markov chains, Gibbs samplers and their possible applications</li>
-<li> Understand linear methods for regression and classification, from ordinary least squares, via Lasso and Ridge to Logistic regression</li>
-<li> Learn about various neural networks and deep  learning methods for supervised and unsupervised learning</li>
-<li> Learn about about decision trees and random forests</li>
-<li> Learn about support vector machines and kernel transformations</li>
-<li> Reduction of data sets, from PCA to clustering, supervised and unsupervided methods</li>
-<li> Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++</li>
+<li> Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, data optimization and machine learning;</li>
+<li> Be capable of extending the acquired knowledge to other systems and cases;</li>
+<li> Have an understanding of central algorithms used in data analysis and machine learning;</li>
+<li> Understand linear methods for regression and classification, from ordinary least squares, via Lasso and Ridge to Logistic regression;</li>
+<li> Learn about neural networks and deep  learning methods for supervised and unsupervised learning. Emphasis on feed forward neural networks, convolutional and recurrent neural networks;</li> 
+<li> Learn about about decision trees, random forests, bagging and boosting methods;</li>
+<li> Learn about support vector machines and kernel transformations;</li>
+<li> Reduction of data sets, from PCA to clustering;</li>
+<li> Autoencoders and Reinforcement Learning;</li>
+<li> Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++ and/or Fortran (Fortran2003 or later).</li>  
 </ul>
 </div>
 
@@ -270,22 +288,34 @@ Projects are handed in using devilry.ifi.uio.no. We use Github as repository for
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 
-<h2 id="___sec6">Topics covered in this course: Statistical analysis and optimization of data </h2>
+<h2 id="___sec7">Topics covered in this course: Statistical analysis and optimization of data </h2>
+
+<p>
+The course has two central parts
+
+<ol>
+<li> Statistical analysis and optimization of data</li>
+<li> Machine learning</li>
+</ol>
+
+These topics will be scattered thorughout the course and may not  necessarily be taught separately. Rather, we will often take an approach (during the lectures and project/exercise sessions) where say elements from statistical data analysis are mixed with specific Machine Learning algorithms
 
 <p>
 <div class="alert alert-block alert-block alert-text-normal">
-<b></b>
+<b>Statistical analysis and optimization of data.</b>
 <p>
 
+<p>
+The following topics will be covered
+
 <ul>
-<li> Basic concepts, expectation values, variance, covariance, correlation functions and errors</li>
-<li> Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions</li>
-<li> Central elements of Bayesian statistics and modeling</li>
-<li> Gradient methods for data optimization</li>
-<li> Monte Carlo methods, Markov chains, Metropolis-Hastings algorithm</li>
-<li> Linear methods for regression and classification</li>
-<li> Estimation of errors using cross-validation, blocking, bootstrapping and jackknife methods</li>
-<li> Practical optimization using Singular-value decomposition and least squares for parameterizing data</li>
+<li> Basic concepts, expectation values, variance, covariance, correlation functions and errors;</li>
+<li> Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;</li>
+<li> Central elements of Bayesian statistics and modeling;</li>
+<li> Gradient methods for data optimization,</li> 
+<li> Monte Carlo methods, Markov chains, Gibbs sampling and Metropolis-Hastings sampling;</li>
+<li> Estimation of errors and resampling techniques such as the cross-validation, blocking, bootstrapping and jackknife methods;</li>
+<li> Principal Component Analysis (PCA) and its mathematical foundation</li>
 </ul>
 </div>
 
@@ -293,7 +323,7 @@ Projects are handed in using devilry.ifi.uio.no. We use Github as repository for
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 
-<h2 id="___sec7">Topics covered in this course: Machine Learning </h2>
+<h2 id="___sec8">Topics covered in this course: Machine Learning </h2>
 
 <p>
 <div class="alert alert-block alert-block alert-text-normal">
@@ -302,21 +332,25 @@ Projects are handed in using devilry.ifi.uio.no. We use Github as repository for
 The following topics will be covered
 
 <ul>
-<li> Linear Regression and Logistic Regression</li>
-<li> Neural networks and deep learning</li>
-<li> Decisions trees and nearest neighbor algorithms</li>
+<li> Linear Regression and Logistic Regression;</li>
+<li> Neural networks and deep learning, including convolutional and recurrent neural networks</li>
+<li> Decisions trees, Random Forests, Bagging and Boosting</li>
 <li> Support vector machines</li>
-<li> Bayesian Neural Networks</li>
+<li> Bayesian linear and logistic regression</li>
 <li> Boltzmann Machines</li>
-<li> Dimensionality reduction, from PCA to cluster models</li>
+<li> Unsupervised learning Dimensionality reduction, from PCA to cluster models</li>
 </ul>
+
+Hands-on demonstrations, exercises and projects aim at deepening your understanding of these topics.
+
+
 </div>
 
 
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 
-<h2 id="___sec8">Extremely useful tools, strongly recommended </h2>
+<h2 id="___sec9">Extremely useful tools, strongly recommended </h2>
 
 <p>
 <div class="alert alert-block alert-block alert-text-normal">
@@ -324,9 +358,8 @@ The following topics will be covered
 <p>
 
 <ul>
-  <li> GIT for version control, highly recommended</li>
-  <li> Devilry for handing in projects, next week</li>
-  <li> Anaconda and other Python environments, see intro slides</li>
+  <li> GIT for version control, and GitHub or GitLab as repositories, highly recommended. This will be discussed during the first exercise session</li>
+  <li> Anaconda and other Python environments, see intro slides and first exercise session</li>
 </ul>
 </div>
 
@@ -334,7 +367,7 @@ The following topics will be covered
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 
-<h2 id="___sec9">Other courses on Data science and Machine Learning  at UiO </h2>
+<h2 id="___sec10">Other courses on Data science and Machine Learning  at UiO </h2>
 
 <p>
 The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/" target="_blank"><tt>https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/</tt></a>  gives an excellent overview of courses on Machine learning at UiO.
@@ -358,7 +391,7 @@ The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus
 
 
 <center style="font-size:80%">
-<!-- copyright --> &copy; 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
+<!-- copyright --> &copy; 1999-2020, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
 </center>
 
 
diff --git a/doc/pub/Intro2Course/ipynb/ipynb-Intro2Course-src.tar.gz b/doc/pub/Intro2Course/ipynb/ipynb-Intro2Course-src.tar.gz
index 4de62b538..818e2c083 100644
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diff --git a/doc/pub/Intro2Course/pdf/Intro2Course-minted.pdf b/doc/pub/Intro2Course/pdf/Intro2Course-minted.pdf
index 521775729..166ed821e 100644
Binary files a/doc/pub/Intro2Course/pdf/Intro2Course-minted.pdf and b/doc/pub/Intro2Course/pdf/Intro2Course-minted.pdf differ
diff --git a/doc/src/Intro2Course/Intro2Course.do.txt b/doc/src/Intro2Course/Intro2Course.do.txt
index c27139655..1da9ab4bc 100644
--- a/doc/src/Intro2Course/Intro2Course.do.txt
+++ b/doc/src/Intro2Course/Intro2Course.do.txt
@@ -8,10 +8,10 @@ DATE: today
 ===== Overview of first week =====
 
 !bblock
-  * Thursday August 22: First lecture: Presentation of the course, aims and content
-  * Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra
-  * Friday August 23: Linear regression   
-  * Computer lab: Tuesday. First time: Tuesday August 27. 
+  * Thursday August 20: First lecture: Presentation of the course, aims and content
+  * Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra and elements of statistics
+  * Friday August 21: Linear regression   
+  * Computer lab: Wednesdays, 8am-6pm. First time: Wednesday August 26. 
 !eblock
 
 
@@ -19,12 +19,10 @@ DATE: today
 ===== Lectures and ComputerLab =====
 
 !bblock
-  * Lectures: Thursday (2.15pm-4pm, this may change) and Friday (12.15pm-2pm).
-  * Weekly reading assignments needed to solve projects and exercises.
+  * Lectures: Thursday (12.15pm-2pm  and Friday (12.15pm-2pm).  Due to the present COVID-19 situation all lectures will be online. They will be recorded and posted online at the official UiO "website":"https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/index.html".
+  * Weekly reading assignments and videos needed to solve projects and exercises.
   * Weekly exercises when not working on projects. You can hand in exercises if you want.
-  * First hour of each lab session may be used to discuss technicalities, address questions etc linked with projects and exercises.
   * Detailed lecture notes, exercises, all programs presented, projects etc can be found at the homepage of the course.
-  * Computerlab: Tuesday (8am-4pm), VB IT-auditorium 3. Depending on how many enlist we may extend the lab sessions
   * Weekly plans and all other information are on the official webpage.
   * No final exam, three projects that are graded and have to be approved. 
 !eblock
@@ -49,29 +47,24 @@ DATE: today
 
 
 !split
-===== Teachers and ComputerLab =====
+===== Teachers  =====
 
 !bblock
 
 _Teachers :_
+* Morten Hjorth-Jensen, morten.hjorth-jensen@fys.uio.no
+  * _Phone_: +47-48257387
+  * _Office_: Department of Physics, University of Oslo, Eastern wing, room FØ470 
+  * _Office hours_: *Anytime*! In Fall Semester 2020 (FS20), as a rule of thumb office hours are planned via computer or telephone. Individual or group office hours will be performed via zoom. Feel free to send an email for planning. In person meetings may also be possible if allowed by the University of Oslo's COVID-19 instructions.
 
-o "Hanna Svennevik":"https://www.researchgate.net/profile/Hanna_Svennevik"
-o "Morten Hjorth-Jensen":"http://mhjgit.github.io/info/doc/web/"
-o "Lucas Charpentier":"https://no.linkedin.com/in/lucas-charpentier-176206171"
-o "Stian Bilek":"https://www.researchgate.net/profile/Stian_Bilek"
-o "Øyvind Sigmundson Schøyen":"https://github.com/Schoyen"
+* Øyvind Sigmundson Schøyen, oyvinssc@student.matnat.uio.no
+  * _Office_: Department of Physics, University of Oslo, Eastern wing, room FØ452
+* Michael Bitney, m.s.bitney@fys.uio.no
+* Kristian Wold, kriswold@student.matnat.uio.no
+* Nicolai Haug, nicoha@student.matnat.uio.no
+* Per-Dimitri Sønsteland, perdimitri.bs@gmail.com
 
 
-
-|------------------------------------------------------|
-|      day         |        Time |
-|----------------------------------------------------|
-| Group 1: Tuesday | 8am-10am |
-| Group 2: Tuesday | 10am-12pm |  
-| Group 3: Tuesday | 12pm-2pm   | 
-| Group 4: Tuesday | 2pm-4pm   | 
-|-------------------------------------------------|
-
 !eblock
 
 !split
@@ -79,46 +72,74 @@ o "Øyvind Sigmundson Schøyen":"https://github.com/Schoyen"
 
 !bblock
 
-o Project 1: September 30 (graded with feedback)
-o Project 2: November 13 (graded with feedback)
-o Project 3: December 15  (graded with feedback)
+o Project 1: September 28 (graded with feedback)
+o Project 2: November 2 (graded with feedback)
+o Project 3: December 7  (graded with feedback)
 
-Projects are handed in using devilry.ifi.uio.no. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via devilry. 
+Projects are handed in using _Canvas_. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via _Canvas_. 
 
 !eblock
 
 
 
+
+!split
+===== Prerequisites =====
+
+Basic knowledge in programming and mathematics, with an emphasis on
+linear algebra. Knowledge of Python or/and C++ as programming
+languages is strongly recommended and experience with Jupiter notebook
+is recommended. Required courses are the equivalents to the University
+of Oslo mathematics courses MAT1100, MAT1110, MAT1120 and at least one
+of the corresponding computing and programming courses INF1000/INF1110
+or MAT-INF1100/MAT-INF1100L/BIOS1100/KJM-INF1100. Most universities
+offer nowadays a basic programming course (often compulsory) where
+Python is the recurring programming language.
+
+
+
 !split
 ===== Learning outcomes =====
 
 !bblock
 
-* Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, data optimization and machine learning
-* Be capable of extending the acquired knowledge to other systems and cases
-* Have an understanding of central algorithms used in data analysis and machine learning
-* Gain knowledge of central aspects of Monte Carlo methods, Markov chains, Gibbs samplers and their possible applications
-* Understand linear methods for regression and classification, from ordinary least squares, via Lasso and Ridge to Logistic regression
-* Learn about various neural networks and deep  learning methods for supervised and unsupervised learning
-* Learn about about decision trees and random forests
-* Learn about support vector machines and kernel transformations
-* Reduction of data sets, from PCA to clustering, supervised and unsupervided methods
-* Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++
+
+This course aims at giving you insights and knowledge about  many of the central algorithms used in Data Analysis and Machine Learning.  The course is project based and through  various numerical projects, normally three, you will be exposed to fundamental research problems in these fields, with the aim to reproduce state of the art scientific results. Both supervised and unsupervised methods will be covered. The emphasis is on a frequentist approach, although we will try to link it with a Bayesian approach as well. You will learn to develop and structure large codes for studying different cases where Machine Learning is applied to, get acquainted with computing facilities and learn to handle large scientific projects. A good scientific and ethical conduct is emphasized throughout the course. More specifically, after this course you will
+
+* Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, data optimization and machine learning;
+* Be capable of extending the acquired knowledge to other systems and cases;
+* Have an understanding of central algorithms used in data analysis and machine learning;
+* Understand linear methods for regression and classification, from ordinary least squares, via Lasso and Ridge to Logistic regression;
+* Learn about neural networks and deep  learning methods for supervised and unsupervised learning. Emphasis on feed forward neural networks, convolutional and recurrent neural networks; 
+* Learn about about decision trees, random forests, bagging and boosting methods;
+* Learn about support vector machines and kernel transformations;
+* Reduction of data sets, from PCA to clustering;
+* Autoencoders and Reinforcement Learning;
+* Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++ and/or Fortran (Fortran2003 or later).  
 
 !eblock
 
 !split
 ===== Topics covered in this course: Statistical analysis and optimization of data =====
 
-!bblock 
-* Basic concepts, expectation values, variance, covariance, correlation functions and errors
-* Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions
-* Central elements of Bayesian statistics and modeling
-* Gradient methods for data optimization
-* Monte Carlo methods, Markov chains, Metropolis-Hastings algorithm
-* Linear methods for regression and classification
-* Estimation of errors using cross-validation, blocking, bootstrapping and jackknife methods
-* Practical optimization using Singular-value decomposition and least squares for parameterizing data
+The course has two central parts
+
+o Statistical analysis and optimization of data
+o Machine learning
+
+These topics will be scattered thorughout the course and may not  necessarily be taught separately. Rather, we will often take an approach (during the lectures and project/exercise sessions) where say elements from statistical data analysis are mixed with specific Machine Learning algorithms
+
+!bblock  Statistical analysis and optimization of data
+
+The following topics will be covered
+* Basic concepts, expectation values, variance, covariance, correlation functions and errors;
+* Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;
+* Central elements of Bayesian statistics and modeling;
+* Gradient methods for data optimization, 
+* Monte Carlo methods, Markov chains, Gibbs sampling and Metropolis-Hastings sampling;
+* Estimation of errors and resampling techniques such as the cross-validation, blocking, bootstrapping and jackknife methods;
+* Principal Component Analysis (PCA) and its mathematical foundation
+
 !eblock
 
 
@@ -127,13 +148,16 @@ Projects are handed in using devilry.ifi.uio.no. We use Github as repository for
 
 !bblock 
 The following topics will be covered
-* Linear Regression and Logistic Regression
-* Neural networks and deep learning
-* Decisions trees and nearest neighbor algorithms
+* Linear Regression and Logistic Regression;
+* Neural networks and deep learning, including convolutional and recurrent neural networks
+* Decisions trees, Random Forests, Bagging and Boosting
 * Support vector machines
-* Bayesian Neural Networks
+* Bayesian linear and logistic regression
 * Boltzmann Machines
-* Dimensionality reduction, from PCA to cluster models
+* Unsupervised learning Dimensionality reduction, from PCA to cluster models
+
+Hands-on demonstrations, exercises and projects aim at deepening your understanding of these topics.
+
 !eblock
 
 
@@ -141,9 +165,8 @@ The following topics will be covered
 ===== Extremely useful tools, strongly recommended =====
 
 !bblock and discussed at the lab sessions
-  * GIT for version control, highly recommended
-  * Devilry for handing in projects, next week
-  * Anaconda and other Python environments, see intro slides
+  * GIT for version control, and GitHub or GitLab as repositories, highly recommended. This will be discussed during the first exercise session
+  * Anaconda and other Python environments, see intro slides and first exercise session
 !eblock
 
 
diff --git a/doc/src/Intro2Course/make.sh b/doc/src/Intro2Course/make.sh
index d908b90b9..9d16b4645 100755
--- a/doc/src/Intro2Course/make.sh
+++ b/doc/src/Intro2Course/make.sh
@@ -47,7 +47,7 @@ system doconce format html $name --html_style=bootstrap --pygments_html_style=de
 system doconce split_html $html.html --method=split --pagination --nav_button=bottom
 
 # IPython notebook
-system doconce format ipynb $name $opt
+#system doconce format ipynb $name $opt
 
 
 # Ordinary plain LaTeX document
diff --git a/doc/src/NeuralNet/ode.py b/doc/src/NeuralNet/ode.py
new file mode 100644
index 000000000..b775e73dd
--- /dev/null
+++ b/doc/src/NeuralNet/ode.py
@@ -0,0 +1,123 @@
+import tensorflow.compat.v1 as tf
+tf.disable_v2_behavior()
+tf.reset_default_graph()
+
+# import necessary packages
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn import datasets
+
+
+# ensure the same random numbers appear every time
+np.random.seed(0)
+
+plt.rcParams['figure.figsize'] = (12,12)
+
+
+# download MNIST dataset
+digits = datasets.load_digits()
+
+# define inputs and labels
+inputs = digits.images
+labels = digits.target
+
+print("inputs = (n_inputs, pixel_width, pixel_height) = " + str(inputs.shape))
+print("labels = (n_inputs) = " + str(labels.shape))
+
+
+# flatten the image
+# the value -1 means dimension is inferred from the remaining dimensions: 8x8 = 64
+n_inputs = len(inputs)
+inputs = inputs.reshape(n_inputs, -1)
+print("X = (n_inputs, n_features) = " + str(inputs.shape))
+
+
+# choose some random images to display
+indices = np.arange(n_inputs)
+random_indices = np.random.choice(indices, size=5)
+
+for i, image in enumerate(digits.images[random_indices]):
+    plt.subplot(1, 5, i+1)
+    plt.axis('off')
+    plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
+    plt.title("Label: %d" % digits.target[random_indices[i]])
+plt.show()
+
+from keras.utils import to_categorical
+from sklearn.model_selection import train_test_split
+
+# one-hot representation of labels
+labels = to_categorical(labels)
+
+# split into train and test data
+train_size = 0.8
+test_size = 1 - train_size
+X_train, X_test, Y_train, Y_test = train_test_split(inputs, labels, train_size=train_size,
+                                                    test_size=test_size)
+
+epochs = 100
+batch_size = 100
+n_neurons_layer1 = 100
+n_neurons_layer2 = 50
+n_categories = 10
+eta_vals = np.logspace(-5, 1, 7)
+lmbd_vals = np.logspace(-5, 1, 7)
+from keras.models import Sequential
+from keras.layers import Dense
+from keras.regularizers import l2
+from keras.optimizers import SGD
+
+def create_neural_network_keras(n_neurons_layer1, n_neurons_layer2, n_categories, eta, lmbd):
+    model = Sequential()
+    model.add(Dense(n_neurons_layer1, activation='sigmoid', kernel_regularizer=l2(lmbd)))
+    model.add(Dense(n_neurons_layer2, activation='sigmoid', kernel_regularizer=l2(lmbd)))
+    model.add(Dense(n_categories, activation='softmax'))
+    
+    sgd = SGD(lr=eta)
+    model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
+    
+    return model
+
+DNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)
+        
+for i, eta in enumerate(eta_vals):
+    for j, lmbd in enumerate(lmbd_vals):
+        DNN = create_neural_network_keras(n_neurons_layer1, n_neurons_layer2, n_categories,eta=eta, lmbd=lmbd)
+        DNN.fit(X_train, Y_train, epochs=epochs, batch_size=batch_size, verbose=0)
+        scores = DNN.evaluate(X_test, Y_test)
+        
+        DNN_keras[i][j] = DNN
+        
+        print("Learning rate = ", eta)
+        print("Lambda = ", lmbd)
+        print("Test accuracy: %.3f" % scores[1])
+        print()
+
+import seaborn as sns
+
+sns.set()
+
+train_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
+test_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
+
+for i in range(len(eta_vals)):
+    for j in range(len(lmbd_vals)):
+        DNN = DNN_keras[i][j]
+
+        train_accuracy[i][j] = DNN.evaluate(X_train, Y_train)[1]
+        test_accuracy[i][j] = DNN.evaluate(X_test, Y_test)[1]
+
+        
+fig, ax = plt.subplots(figsize = (10, 10))
+sns.heatmap(train_accuracy, annot=True, ax=ax, cmap="viridis")
+ax.set_title("Training Accuracy")
+ax.set_ylabel("$\eta$")
+ax.set_xlabel("$\lambda$")
+plt.show()
+
+fig, ax = plt.subplots(figsize = (10, 10))
+sns.heatmap(test_accuracy, annot=True, ax=ax, cmap="viridis")
+ax.set_title("Test Accuracy")
+ax.set_ylabel("$\eta$")
+ax.set_xlabel("$\lambda$")
+plt.show()