diff --git a/02_classification.ipynb b/02_classification.ipynb
index 77fa99885ff1bc6e80b5905b9b10ce537014d7cc..ac272bac000bf261f20f628917471bba63618156 100644
--- a/02_classification.ipynb
+++ b/02_classification.ipynb
@@ -2014,6 +2014,13 @@
     "    In <code>scikit-learn</code> many classifiers support multi-class problems out of the box and also offer functionalities to implement <strong>one-vs-all</strong> or <strong>one-vs-one</strong> in some cases (cf. <a href=\"https://scikit-learn.org/stable/modules/multiclass.html\"><code>scikit-learn</code> multiclass and multilabel algorithms</a>).\n",
     "</p></div>"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (C) 2019 ETH Zurich, SIS ID"
+   ]
   }
  ],
  "metadata": {
@@ -2033,7 +2040,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.7.4"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,
diff --git a/03_overfitting_and_cross_validation.ipynb b/03_overfitting_and_cross_validation.ipynb
index e84f7725bf876af89d3935677d2203476c198193..e2159a70908fda6a1015d8710601f3c01947dd8f 100644
--- a/03_overfitting_and_cross_validation.ipynb
+++ b/03_overfitting_and_cross_validation.ipynb
@@ -1395,6 +1395,13 @@
     "print(\"BEST RESULT CROSS VALIDATION\")\n",
     "print(\"score = {:.3f}  C={:.1f}  gamma={:.1f}\".format(best_score, C, gamma))"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (C) 2019 ETH Zurich, SIS ID"
+   ]
   }
  ],
  "metadata": {
@@ -1414,7 +1421,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.7.4"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,
diff --git a/04_measuring_quality_of_a_classifier.ipynb b/04_measuring_quality_of_a_classifier.ipynb
index ff5da770c14aeae04da8135f05f988c0df8a34b7..1917213f83124a081f8aaf10a45abd8d4e42f3a2 100644
--- a/04_measuring_quality_of_a_classifier.ipynb
+++ b/04_measuring_quality_of_a_classifier.ipynb
@@ -808,6 +808,13 @@
     "classifier.fit(features, labels)\n",
     "predicted = classifier.predict(eval_features)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (C) 2019 ETH Zurich, SIS ID"
+   ]
   }
  ],
  "metadata": {
@@ -827,7 +834,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.7.4"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,
diff --git a/05_preprocessing_pipelines_and_hyperparameter_optimization.ipynb b/05_preprocessing_pipelines_and_hyperparameter_optimization.ipynb
index 8c0d9045b12bb51147a7f813c0e60ca8462e99db..a169eb0b9aa69b0fb8cec0df3e2d006976ba4f0d 100644
--- a/05_preprocessing_pipelines_and_hyperparameter_optimization.ipynb
+++ b/05_preprocessing_pipelines_and_hyperparameter_optimization.ipynb
@@ -1358,7 +1358,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.7.4"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,
diff --git a/06_classifiers_overview.ipynb b/06_classifiers_overview.ipynb
index c4965e4f270f342d6bac34b6b9e05f4419dfb9fe..697b49cad1513ec5d93a7c5cdc96010f5678c8de 100644
--- a/06_classifiers_overview.ipynb
+++ b/06_classifiers_overview.ipynb
@@ -3127,7 +3127,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.7.4"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,
diff --git a/07_regression.ipynb b/07_regression.ipynb
index d575c458305f6c0d9d0946e7c95001d06509cfa9..febedf681168024b0373a2a844e813705b668c10 100644
--- a/07_regression.ipynb
+++ b/07_regression.ipynb
@@ -1395,11 +1395,11 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "Copyright (C) 2019 ETH Zurich, SIS ID"
+   ]
   }
  ],
  "metadata": {
@@ -1419,7 +1419,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.7.4"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,
diff --git a/08_neural_networks.ipynb b/08_neural_networks.ipynb
index 343143b5d27aac9a3456b6371c096f057ebc756a..5702360de410dafa9809a1fa728cc312c666270a 100644
--- a/08_neural_networks.ipynb
+++ b/08_neural_networks.ipynb
@@ -3564,18 +3564,11 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "Copyright (C) 2019 ETH Zurich, SIS ID"
+   ]
   }
  ],
  "metadata": {
@@ -3594,7 +3587,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.7.4"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,
@@ -3613,6 +3606,19 @@
    "latex_user_defs": false,
    "report_style_numbering": false,
    "user_envs_cfg": false
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
   }
  },
  "nbformat": 4,