Add segementation exercise and solution, include in toc

Author: foerster-finsternis

_toc.yml

@@ -53,7 +53,8 @@ parts:
     chapters:
     - file: day3.1_ml-application/readme.md
       sections:
-      - file: day3.1_ml-application/01_notebook.ipynb
+      - file: day3.1_ml-application/01a_segmentation-exercise.ipynb
+      - file: day3.1_ml-application/01b_segmentation-solution.ipynb
 
     - file: day3.2_dl-application/readme.md
       sections:

…ml-application/exercise_clustering.ipynb …lication/01a_segmentation-exercise.ipynbday3.1_ml-application/exercise_clustering.ipynb renamed to day3.1_ml-application/01a_segmentation-exercise.ipynb day3.1_ml-application/exercise_clustering.ipynb renamed to day3.1_ml-application/01a_segmentation-exercise.ipynb

@@ -5,9 +5,9 @@
    "id": "7d5a9eff-f82a-45af-8b48-94e5816ba44d",
    "metadata": {},
    "source": [
-    "# Comprehensive Exercise on Clustering\n",
+    "# Comprehensive Exercise on Segmentation Analysis\n",
     "\n",
-    "One extremely helpful application of clustering is in data exploration. Clustering helps us find fundamental structures in data that is unknown to us, e.g., whether individual samples have any similarities that we do not yet recognize.\n",
+    "One extremely helpful application of Machine Learning is in data exploration and segementation analysis. Clustering algorithms help us find fundamental structures in data that is unknown to us, e.g., whether individual data points have any similarities that we do not yet recognize.\n",
     "\n",
     "Let's try this out on a dataset about demographics, health metrics, and engagement patterns of patients.\n",
     "\n",
@@ -25,8 +25,8 @@
     "* Load the data from the file [patient_segmentation_dataset.csv](patient_segmentation_dataset.csv)\n",
     "* Inspect the data and make yourself familiar with its content, data types, distributions, etc.\n",
     "* Decide for a fitting clustering algorithm based on your data insights (e.g., can KMeans be used for this specific data?)\n",
-    "* Prepare the data for clustering and apply the clustering algorithm\n",
-    "* Explore the clusters (patient segments) to hopefully gain helpful insights\n",
+    "* Prepare the data for clustering and apply the clustering algorithm. Compare different clustering algorithms.\n",
+    "* Explore the clusters (patient segments) to gain helpful insights\n",
     "\n",
     "**Use of the AI assistant**\n",
     "\n",
@@ -38,28 +38,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "f4a890b2-8561-4e66-825e-c570313e4659",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <div style=\"font-size:7pt\">\n",
-       "            This notebook may contain text, code and images generated by artificial intelligence.\n",
-       "            Used model: vllm-llama-4-scout-17b-16e-instruct, vision model: None, endpoint: https://kiara.sc.uni-leipzig.de/api/v1, bia-bob version: 0.34.3.. Do not enter sensitive or private information and verify generated contents according to good scientific practice. Read more: <a href=\"https://github.com/haesleinhuepf/bia-bob#disclaimer\">https://github.com/haesleinhuepf/bia-bob#disclaimer</a>\n",
-       "            </div>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Import bia-bob as a helpful Python & Medical AI expert\n",
     "from bia_bob import bob\n",
@@ -74,30 +56,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "316bc5fa-49fb-4573-9344-b1f01616834f",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "I'm a medical data science AI assistant, an expert in Python programming and data analysis, specializing in tasks like data analysis, feature engineering, machine learning, and deep learning for medical datasets."
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "%bob Who are you ? Just 1 sentence!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "a34a8c79-882d-4804-a7cb-16b6bdcdc62b",
    "metadata": {},
    "outputs": [],