diff --git a/notebooks/machine-learning/answers/0.bias-and-common-errors.ipynb b/notebooks/machine-learning/answers/0.bias-and-common-errors.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..d02f22faef70e5a95e4f1939c0cbfca05a01c4f8
--- /dev/null
+++ b/notebooks/machine-learning/answers/0.bias-and-common-errors.ipynb
@@ -0,0 +1,145 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "411ba7b3-7d56-45fe-b01e-205275e1988a",
+   "metadata": {},
+   "source": [
+    "# Des biais et des erreurs communes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e2fcf4b-d8aa-4bb2-8eab-dfe9a3210604",
+   "metadata": {},
+   "source": [
+    "Les exercices suivants sont destinés à vous familiariser avec les concepts appréhendés lors de l’introduction au *machine learning*. Avant toute chose, importez les librairies utiles :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4bbfb43b-1feb-4366-b1e7-5536f0f5aacd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "sns.set_context('notebook')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61c8d84f-a791-425e-ae70-306f0da93a55",
+   "metadata": {},
+   "source": [
+    "## Les relations à distance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "057d738a-a8a8-4d38-9dd2-b109d1325308",
+   "metadata": {},
+   "source": [
+    "Il paraît que l’univers est en expansion et que cette expansion va en s’accélérant. C’est en tout cas ce que l’étude de Wendy Freedman et al. a prouvé ([*Freedman, 2001*](../0.about-datasets.ipynb#Stellar-Objects)). Par conséquent, on s’attend à ce qu’un objet stellaire s’éloigne d’autant plus vite de nous que la distance qui nous sépare de lui est grande.\n",
+    "\n",
+    "Chargeons le jeu de données en se concentrant sur des objets proches de nous (entre 30 000 et 100 000 années-lumières) :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1cf3ab56-418f-46e3-bc3f-36cf0eec0dbf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load data\n",
+    "df = pd.read_csv(\"../files/stellar-objects.csv\", sep=\"\\t\")\n",
+    "\n",
+    "# distance: megaparsec (MPC)\n",
+    "# velocity: in km/s\n",
+    "df[\"velocity\"] = df.v_helio.fillna(df.v_flow.fillna(df.v_cmb))\n",
+    "\n",
+    "# objects close to earth, but not that close :)\n",
+    "data = df[(df.distance > 10) & (df.distance < 30)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0a306e1-be3e-4431-84a3-32216340c326",
+   "metadata": {},
+   "source": [
+    "Affichons un nuage de points afin de vérifier la proposition de ces pontes de la NASA :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1fb0d73f-62bd-4777-b4e4-276554e2a599",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.scatterplot(data=data, x=\"distance\", y=\"velocity\")\n",
+    "\n",
+    "sns.despine()\n",
+    "\n",
+    "plt.title(\"Relation between distance and velocity of stellar objects\")\n",
+    "plt.xlabel(\"Distance (MPC)\")\n",
+    "plt.ylabel(\"Velocity (km/s)\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbb20849-4a22-4870-940b-8067fd06e548",
+   "metadata": {},
+   "source": [
+    "Rien de bien concluant à première vue, non ? Afin de déterminer visuellement s’il existe bien une relation linéaire entre la distance et la vitesse d’éloignement, affichez une droite de régression :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "125c4241-faf9-4209-b8c6-cfc2c1b07105",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# your code here\n",
+    "\n",
+    "_ = sns.regplot(data=data, x=\"distance\", y=\"velocity\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa3c4eeb-5ce4-44f2-9403-9d50a9e425e9",
+   "metadata": {},
+   "source": [
+    "Bon, appelez BFM TV, Wendy s’est trompée : 2/3 des points sont en dehors de l’intervalle de confiance à 95 %. Ou alors, peut-être avons-nous fait une erreur de méthodologie ?"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/machine-learning/stellar-objects.ipynb b/notebooks/machine-learning/stellar-objects.ipynb
deleted file mode 100644
index 816a825d12ff8d25388b1930e8527ccaf7dd0d81..0000000000000000000000000000000000000000
--- a/notebooks/machine-learning/stellar-objects.ipynb
+++ /dev/null
@@ -1,86 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4bbfb43b-1feb-4366-b1e7-5536f0f5aacd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd\n",
-    "import seaborn as sns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1cf3ab56-418f-46e3-bc3f-36cf0eec0dbf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# distance: megaparsec (MPC)\n",
-    "# velocity: in km/s\n",
-    "df = pd.read_csv(\"./galaxies.csv\", sep=\"\\t\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f6e8f95c-1da6-4d6f-b8c0-a6aa5cdddc2d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df[\"velocity\"] = df.v_helio.fillna(df.v_flow.fillna(df.v_cmb))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8396a4ef-9f1f-425a-886e-8d2bf3d979ee",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.title(\"Relation between distance and velocity of stellar objects\")\n",
-    "plt.xlabel(\"Distance (MPC)\")\n",
-    "plt.ylabel(\"Velocity (km/s)\")\n",
-    "\n",
-    "#sns.scatterplot(data=df, x=\"distance\", y=\"velocity\", color=\"orange\")\n",
-    "sns.regplot(data=df, x=\"distance\", y=\"velocity\", color=\"orange\")\n",
-    "\n",
-    "sns.despine()\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6d94f18a-29ec-4da1-b542-b498e3017d2d",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}