diff --git a/ML_U3_1_Perceptron.ipynb b/ML_U3_1_Perceptron.ipynb
index 487c60e..347708c 100644
--- a/ML_U3_1_Perceptron.ipynb
+++ b/ML_U3_1_Perceptron.ipynb
@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -82,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -153,7 +153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [
     {
@@ -167,18 +167,18 @@
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-       "[<matplotlib.lines.Line2D at 0x7f7e777bfa50>]"
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      "metadata": {},
-     "execution_count": 10
+     "execution_count": 86
     },
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      "output_type": "display_data",
      "data": {
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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -204,21 +204,29 @@
     "# Geradengleichung berechnen und plotten\n",
     "weights = perceptron_AND.getWeights()\n",
     "print(weights)\n",
-    "x = list()\n",
-    "disc = list()\n",
-    "for i in range(len(train_input_AND)):\n",
-    "    x.append(train_input_AND[i].dot(2**np.arange(train_input_AND[i].size)[::-1]))\n",
-    "    sum = weights[0]\n",
-    "    for j in range(len(train_input_AND[i])):\n",
-    "        sum -= 2**j * train_input_AND[i,-j-1] * weights[j+1]\n",
-    "    disc.append(sum)\n",
-    "plt.plot(x,labels_AND,'o')\n",
-    "plt.plot(x,disc)\n"
+    "#x = list()\n",
+    "#disc = list()\n",
+    "#for i in range(len(train_input_AND)):\n",
+    "#    x.append(train_input_AND[i].dot(2**np.arange(train_input_AND[i].size)[::-1]))\n",
+    "#    sum = weights[0]\n",
+    "#    for j in range(len(train_input_AND[i])):\n",
+    "#        sum -= 2**j * train_input_AND[i,-j-1] * weights[j+1]\n",
+    "#    disc.append(sum)\n",
+    "#plt.plot(x,labels_AND,'o')\n",
+    "#plt.plot(x,disc)\n",
+    "\n",
+    "x_AND = [1-weights[1],1]\n",
+    "y_AND = [1+weights[0],1-weights[2]+weights[0]]\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.scatter(train_input_AND[(labels_AND==-1),0] , train_input_AND[(labels_AND==-1),1])\n",
+    "ax.scatter(train_input_AND[(labels_AND==1),0] , train_input_AND[(labels_AND==1),1])\n",
+    "plt.plot(x_AND,y_AND)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [
     {
@@ -232,18 +240,18 @@
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f7e76e0b850>]"
+       "[<matplotlib.lines.Line2D at 0x7fa6ce45ab90>]"
       ]
      },
      "metadata": {},
-     "execution_count": 11
+     "execution_count": 87
     },
     {
      "output_type": "display_data",
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -269,21 +277,19 @@
     "# Geradengleichung berechnen und plotten\n",
     "weights = perceptron_OR.getWeights()\n",
     "print(weights)\n",
-    "x = list()\n",
-    "disc = list()\n",
-    "for i in range(len(train_input_OR)):\n",
-    "    x.append(train_input_OR[i].dot(2**np.arange(train_input_OR[i].size)[::-1]))\n",
-    "    sum = weights[0]\n",
-    "    for j in range(len(train_input_OR[i])):\n",
-    "        sum -= 2**j * train_input_OR[i,-j-1] * weights[j+1]\n",
-    "    disc.append(sum)\n",
-    "plt.plot(x,labels_OR,'o')\n",
-    "plt.plot(x,disc)"
+    "\n",
+    "x_OR = [1-weights[1],1]\n",
+    "y_OR = [1+weights[0], 1-weights[2]+weights[0]]\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.scatter(train_input_OR[(labels_OR==-1),0] , train_input_OR[(labels_OR==-1),1])\n",
+    "ax.scatter(train_input_OR[(labels_OR==1),0] , train_input_OR[(labels_OR==1),1])\n",
+    "plt.plot(x_OR,y_OR)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [
     {
@@ -297,18 +303,18 @@
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f7e7678cd90>]"
+       "[<matplotlib.lines.Line2D at 0x7fa6ce533ad0>]"
       ]
      },
      "metadata": {},
-     "execution_count": 13
+     "execution_count": 88
     },
     {
      "output_type": "display_data",
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -334,16 +340,14 @@
     "# Geradengleichung berechnen und plotten\n",
     "weights = perceptron_XOR.getWeights()\n",
     "print(weights)\n",
-    "x = list()\n",
-    "disc = list()\n",
-    "for i in range(len(train_input_XOR)):\n",
-    "    x.append(train_input_XOR[i].dot(2**np.arange(train_input_XOR[i].size)[::-1]))\n",
-    "    sum = weights[0]\n",
-    "    for j in range(len(train_input_XOR[i])):\n",
-    "        sum -= 2**j * train_input_XOR[i,-j-1] * weights[j+1]\n",
-    "    disc.append(sum)\n",
-    "plt.plot(x,labels_XOR,'o')\n",
-    "plt.plot(x,disc)"
+    "\n",
+    "x_XOR = [1-weights[1],1]\n",
+    "y_XOR = [1+weights[0], 1-weights[2]+weights[0]]\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.scatter(train_input_XOR[(labels_XOR==-1),0] , train_input_XOR[(labels_XOR==-1),1])\n",
+    "ax.scatter(train_input_XOR[(labels_XOR==1),0] , train_input_XOR[(labels_XOR==1),1])\n",
+    "plt.plot(x_XOR,y_XOR)"
    ]
   },
   {
@@ -357,26 +361,29 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Antwort:"
+    "**Antwort:**\n",
+    "Das Bias stellt einen Schwellwert dar. \n",
+    "Die Summe der Produkte von Eingabewerten und ihren Gewichtungen müssen diesen Schwellwert überschreiten, damit das Perzepton einen Effekt hat."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Aufgabe 4:** Wenden Sie das Perzeptron auf das Problem der Banknotenklassifizierung der letzten Übung an. Wählen und berechnen Sie dafür wieder zwei geeignete Merkmale der Trainingsbanknoten (Momentenberechnung auf den Farbkanälen mit  `banknotes[i].compute_feature(moment, color)`). Mit welcher Genauigkeit (engl. *accuracy*) werden die Testbanknoten klassifiziert? Wie sind die erreichten Ergebnisse des Perzeptrons im Vergleich zum linearen Klassifikator der letzten Übung zu bewerten?"
+    "**Aufgabe 4:** Wenden Sie das Perzeptron auf den Iris-Datensatz der letzten Übung an.\n",
+    "Wählen Sie dazu wieder zwei Merkmale und zwei Zielklassen des Datensatzes. Wie gut funktioniert Ihr Perzeptron?\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [
     {
      "output_type": "display_data",
      "data": {
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\n"
      },
      "metadata": {
@@ -416,7 +423,7 @@
     "X_train, X_test, y_train, y_test = (\n",
     "    train_test_split(X, y, test_size=.2, random_state=np.random.seed(42)))\n",
     "\n",
-    "# Scatterplot der ausgewählten Merkmale und Klassen\n",
+    "# Scatterplot der ausgewählten Merkmale und Klie das Perzeptron auf das Problem der Banknotenklassifizierung der letzten Übung an. Wählen und berechnen Sie dafür wieder zwei geeignete Merkmale der Trainingsbanknoten (Momentenberechnung auf den Farbkanälen mit  `banknotes[i].compute_feature(moment, color)`). Mit welcher Genauigkeit (engl. *accuracy*) werden die Testbanknoten klassifiziert? Wie sind die erreichten Ergebnisse des Perzeptrons im Vergleich zum linearen Klassifikator der letzten Übung zu bewerten?assen\n",
     "fig, ax = plt.subplots()\n",
     "ax.scatter(X[(y==-1),0] , X[(y==-1),1])\n",
     "ax.scatter(X[(y==1),0] , X[(y==1),1])\n",
@@ -426,26 +433,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 96,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[ 0.38 -0.12  0.14]\n"
+     ]
+    }
+   ],
    "source": [
     "# Perzeptron auf Iris-Datensatz trainieren und anwenden\n",
-    "# Dein Code kommt hierhin:"
+    "# Dein Code kommt hierhin:\n",
+    "percepton_iris = Perceptron(2, 100, 0.1)\n",
+    "percepton_iris.fit(X_train,y_train)\n",
+    "\n",
+    "weights = percepton_iris.getWeights()\n",
+    "print(weights)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Aufgabe 5:** Welchen Einfluss haben die Hyperparameter *Epoche* und *Lernrate* auf die Klassifizierung der Banknoten? Lassen sich die vorherigen Ergebnisse noch verbessern?"
+    "**Aufgabe 5:** Welche(n) Nachteil(e) könnte die symmetrische Aktualisierungsfunktion in Bezug\n",
+    "auf das Anlernen der Gewichte haben?"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Antwort:"
+    "**Antwort:** \n",
+    "- Ausreißer haben einen großen Effekt auf die Gewichtung\n",
+    "- Die Lernfähigkeit lässt nach"
    ]
   }
  ],