diff --git a/00_Quickstart.ipynb b/00_Quickstart.ipynb
deleted file mode 100644
index b6358016b14bfbda09139fcbaa40fc9bbec1dee5..0000000000000000000000000000000000000000
--- a/00_Quickstart.ipynb
+++ /dev/null
@@ -1,169 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![IRT Logo](https://www.irt.rwth-aachen.de/global/show_picture.asp?id=aaaaaaaaaanuwoa)\n",
-    "\n",
-    "# FML (Fundamentals of Machine Learning Course) Profile Quickstart\n",
-    "\n",
-    "Welcome to the FML profile on JupyterLab!\n",
-    "This notebook should introduce you to the basic functionalities of the JupyterLab.\n",
-    "\n",
-    "* Execute a single cell: <span class=\"fa-play fa\"></span>\n",
-    "* Execute all cells: Menu: Run <span class=\"fa-chevron-right fa\"></span> Run All Cells\n",
-    "* To reboot kernel: <span class=\"fa-refresh fa\"></span>\n",
-    "\n",
-    "Find more in the reference (menu: Help <span class=\"fa-chevron-right fa\"></span> Jupyter Reference)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Creating a new cell\n",
-    "\n",
-    "You can choose the type of a new cell between _Code_ or _Markdown_. _Code_ is for Python code, and _Markdown_ is a text cell.\n",
-    "For example, you are currently reading the content of a Markdown cell!\n",
-    "\n",
-    "### Markdown cells\n",
-    "\n",
-    "Running a markdown cell parses the text and displays it in a more readable mode.\n",
-    "You can also edit the content of a Markdown cell by double clicking on it.\n",
-    "\n",
-    "Markdown supports lists, images, LaTeX equations, and much more.\n",
-    "\n",
-    "#### Lists\n",
-    "\n",
-    "* Like\n",
-    "* this\n",
-    "  1. We can even nest them like\n",
-    "  2. this!\n",
-    "* Isn't that wonderfull?\n",
-    "  \n",
-    "#### Images \n",
-    "\n",
-    "![Newtons cradle](https://upload.wikimedia.org/wikipedia/commons/thumb/d/d3/Newtons_cradle_animation_book_2.gif/200px-Newtons_cradle_animation_book_2.gif)\n",
-    "\n",
-    "#### LaTeX equations\n",
-    "\n",
-    "$$\\mathrm{e}^{\\mathrm{j} x} = \\cos(x)+\\mathrm{j}\\sin(x)$$\n",
-    "\n",
-    "### Further reading\n",
-    "Read more in the reference (menu: Help <span class=\"fa-chevron-right fa\"></span> Markdown Reference)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Python cells\n",
-    "\n",
-    "You can enter code in these cells, and running the cell runs the corresponding code."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(\"Hello world!\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Example\n",
-    "\n",
-    "Execute the cell below to see the contents of variable `a`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "a = np.array([0, 1, -5])\n",
-    "print(a)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plots with matplotlib\n",
-    "\n",
-    "Here is a nice matplotlib [tutorial](https://matplotlib.org/tutorials/introductory/usage.html#sphx-glr-tutorials-introductory-usage-py)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "fs = 44100;\n",
-    "(t, deltat) = np.linspace(-1, 1, 20*fs, retstep=True) # t axis in seconds\n",
-    "\n",
-    "fig,ax = plt.subplots(); ax.grid();\n",
-    "ax.plot(t, np.sin(2*np.pi*t))\n",
-    "ax.set_xlabel(r'$t$'); ax.set_ylabel(r'$s(t) = \\sin(2 \\pi t)$'); "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " Copyright (c) 2024, Institute of Automatic Control - RWTH Aachen University\n",
-    " All rights reserved. "
-   ]
-  }
- ],
- "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": "3.9.7"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "85b74e45f3169d6039abd91b8eff61b3ebc8a01e856c123760a52652b4514477"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/01_JupyterLab.ipynb b/01_JupyterLab.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..d0640fedaaf924e7e64967a382ec2c63f5379d53
--- /dev/null
+++ b/01_JupyterLab.ipynb
@@ -0,0 +1,172 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![IRT Logo](https://www.irt.rwth-aachen.de/global/show_picture.asp?id=aaaaaaaaaanuwoa)\n",
+    "\n",
+    "# JupyterLab Quickstart\n",
+    "\n",
+    "This notebook should introduce you to the basic functionalities of JupyterLab.\n",
+    "\n",
+    "* Execute a single cell: ▶️\n",
+    "* Execute all cells: Menu: Run → Run All Cells (or ⏩ which includes restart of kernel)\n",
+    "* To reboot kernel: 🔄\n",
+    "\n",
+    "Find more in the reference (menu: Help → JupyterLab Reference)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating a new cell\n",
+    "\n",
+    "You can choose the type of a new cell between _Code_ or _Markdown_. _Code_ is for Python code, and _Markdown_ is a text cell.\n",
+    "For example, you are currently reading the content of a Markdown cell!\n",
+    "\n",
+    "### Markdown cells\n",
+    "\n",
+    "Running a markdown cell parses the text and displays it in a more readable mode.\n",
+    "You can also edit the content of a Markdown cell by double clicking on it.\n",
+    "\n",
+    "Markdown supports lists, images, LaTeX equations, and much more.\n",
+    "\n",
+    "#### Lists\n",
+    "\n",
+    "* Like\n",
+    "* this\n",
+    "  1. We can even nest them like\n",
+    "  2. this!\n",
+    "* Isn't that wonderfull?\n",
+    "  \n",
+    "#### Images \n",
+    "\n",
+    "![Newtons cradle](https://upload.wikimedia.org/wikipedia/commons/thumb/d/d3/Newtons_cradle_animation_book_2.gif/200px-Newtons_cradle_animation_book_2.gif)\n",
+    "\n",
+    "#### LaTeX equations\n",
+    "\n",
+    "$$\\mathrm{e}^{\\mathrm{j} x} = \\cos(x)+\\mathrm{j}\\sin(x)$$\n",
+    "\n",
+    "### Further reading\n",
+    "Read more in the reference (menu: Help <span class=\"fa-chevron-right fa\"></span> Markdown Reference)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Python cells\n",
+    "\n",
+    "You can enter code in these cells, and running the cell runs the corresponding code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello world!\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Hello world!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Example\n",
+    "\n",
+    "Execute the cell below to see the contents of variable `a`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0  1 -5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "a = np.array([0, 1, -5])\n",
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Copyright (c) 2024, Institute of Automatic Control - RWTH Aachen University\n",
+    " All rights reserved. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tasks\n",
+    "The goal of the following tasks is to make you familiar with the JupyterLab user interface.\n",
+    "\n",
+    "## Task 1 - Getting to know JupyterLab and Notebooks\n",
+    "* Restart the kernel and run the whole notebook.\n",
+    "* Add a new Code Cell below with the following lines:\n",
+    "  ```python\n",
+    "  b = 1.5 * a\n",
+    "  b\n",
+    "  ```\n",
+    "* The notebook will always output the return value of the last line - collapse / hide the output.\n",
+    "* Markdown cells are a good way to document your thoughts - add a markdown cell above the last cell and describe the calculation.\n",
+    "* Open the *JupyterLab reference* and find the *The JupyterLab Interface* section.\n",
+    "\n",
+    "## Task 2 - Working with cells\n",
+    "* Notebooks are **stateful** - this means that it saves the value of variables in a global scope until the kernel is restarted. Thus, the order of execution matters.\n",
+    "  * Have a look at the *Notebooks* section in the *JupyterLab reference*.\n",
+    "  * Drag & drop the cell containingcalculation of `b` to the top of the notebook and run the whole notebook without restarting the kernel.\n",
+    "  * Now restart the kernel and run the whole notebook.\n",
+    "  * What happened? Fix the error by moving the calculation of `b` to an appropriate place.\n"
+   ]
+  }
+ ],
+ "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": "3.12.2"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "85b74e45f3169d6039abd91b8eff61b3ebc8a01e856c123760a52652b4514477"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/02_PythonSetup.ipynb b/02_PythonSetup.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..ef9a750e1e353313b6704a7e598f548c74cde6aa
--- /dev/null
+++ b/02_PythonSetup.ipynb
@@ -0,0 +1,42 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![IRT Logo](https://www.irt.rwth-aachen.de/global/show_picture.asp?id=aaaaaaaaaanuwoa)\n",
+    "\n",
+    "# Python Setup\n",
+    "Jupyter notebook providers like the RWTH, Google with Colab, or Kaggel notebooks typically serve environments which contain everything you need:\n",
+    "* A python installation\n",
+    "* Common packages like numpy, matplotlib, or PyTorch preinstalled\n"
+   ]
+  }
+ ],
+ "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": "3.12.2"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "85b74e45f3169d6039abd91b8eff61b3ebc8a01e856c123760a52652b4514477"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}