diff --git a/is/UB3/ExerciseSheet3Max.ipynb b/is/UB3/ExerciseSheet3Max.ipynb
index 4319b28..536dde8 100644
--- a/is/UB3/ExerciseSheet3Max.ipynb
+++ b/is/UB3/ExerciseSheet3Max.ipynb
@@ -54,11 +54,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 294,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      ":0: FutureWarning: IPython widgets are experimental and may change in the future.\n"
+     ]
+    }
+   ],
    "source": [
     "# Loading the required packages\n",
     "%matplotlib inline\n",
@@ -70,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 295,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -94,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 296,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
@@ -118,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 297,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false,
     "scrolled": false
@@ -127,10 +135,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x107627890>"
+       "<matplotlib.text.Text at 0x105161310>"
       ]
      },
-     "execution_count": 297,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -264,7 +272,7 @@
        "q+HCYGZmNf4fBBSJ6m7pJCYAAAAASUVORK5CYII=\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x107742310>"
+       "<matplotlib.figure.Figure at 0x10511c210>"
       ]
      },
      "metadata": {},
@@ -288,7 +296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 305,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
@@ -316,7 +324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 306,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -324,7 +332,7 @@
    "source": [
     "def MSE(y_predict,y_true):\n",
     "  ## Implement mean squared error for a given input y and its predictions.\n",
-    "  return np.linalg.norm(y_predict-y_true)"
+    "  return (np.linalg.norm(y_predict-y_true)**2)/y_predict.shape[0]"
    ]
   },
   {
@@ -336,7 +344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 314,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -346,198 +354,204 @@
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       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10891a750>"
+       "<matplotlib.figure.Figure at 0x10586f5d0>"
       ]
      },
      "metadata": {},
@@ -631,22 +645,295 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 327,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
-     "ename": "ValueError",
-     "evalue": "shapes (8,10) and (6,) not aligned: 10 (dim 1) != 6 (dim 0)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-327-96e4905b3c2b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0myt\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconstruct_poly\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwRidge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0minteract\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-295-aa77e456f942>\u001b[0m in \u001b[0;36mwRidge\u001b[0;34m(X, y, lamb)\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;31m#print y\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;31m#print np.dot(np.dot(np.linalg.inv((np.dot(X.T,X)+lamb*np.identity(X.shape[-1]))),X.T),y)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mlamb\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0midentity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m: shapes (8,10) and (6,) not aligned: 10 (dim 1) != 6 (dim 0)"
-     ]
+     "data": {
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+       "/im4nvgo4Fb1vDW5tl8H3IPNpW5MWrIee2lSHQ4cVncDrdc/xAkj3uRMYDUwVaLyvESlWezuEpWW\n",
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+       "gMlhxVIQETkd9zHtnOCEUFmQanXC74BWItJcRCrhZgD9MOSYyiQREeBl4CdVfTrsePIjInVFpHbw\n",
+       "c1XgFFLwb1xV71LVpqq6N3AJ8HkqJnURqSYiNYOfqwOn4gZM7CLsGvsjIvJjUIPzgH4hx5OfgbiT\n",
+       "u2ODYUbPhR1QXvK7aCwVqOpW4EbgU9zIg7dU9edwo9qViAwDvgT2FZF5IhJKabAQbYHLcaNMJgdf\n",
+       "qTiSpxHwefD3/TUwUlU/CzmmeKRq2bABMCnm9fxIVcfktaNdoGSMMWkm7B67McaYBLPEbowxacYS\n",
+       "uzHGpBlL7MYYk2YssRtjTJqxxG6MMWnGErsxxqQZS+zGGJNm/h9LRNvNs+E2pQAAAABJRU5ErkJg\n",
+       "gg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1058dd2d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -658,11 +945,14 @@
     "w = wRidge(X,y,1.0)\n",
     "@interact(n=[1,16])\n",
     "def plot(n):\n",
-    "  #X = construct_poly(x,n) #n=d\n",
-    "  #w = wRidge(X,yt,1.0)\n",
-    "  plt.plot(xt,X.dot(w))\n",
-    "  plt.title(\"MSE %f\" % MSE(X.dot(w),yt))\n",
+    "  X = construct_poly(x,n)  \n",
+    "  Xt = construct_poly(xt,n) #n=d\n",
+    "  w = wRidge(X,y,1.0)\n",
+    "  plt.plot(x,X.dot(w))\n",
+    "  plt.plot(xt,Xt.dot(w))\n",
+    "  plt.title(\"MSE train: %f  test %f\" % (MSE(X.dot(w),y),MSE(Xt.dot(w),yt)))\n",
     "  plt.plot(xt,yt)\n",
+    "  plt.plot(x,y)\n",
     "# was ist mit fehler kurven gemeint?"
    ]
   },
@@ -674,6 +964,301 @@
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.75123654094\n",
+      "0.781528050024\n",
+      "11.0428184273\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": [
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+       "ZondGGPSjCV2Y4xJM5bYjTEmzVhiN8aYNGOJ3Rhj0owldmOMSTOW2I0xJs38P+KRKkqlz/nMAAAA\n",
+       "AElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x105aad0d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "def plot(n):\n",
+    "    X = construct_poly(x,6)  \n",
+    "    Xt = construct_poly(xt,6) #n=d\n",
+    "    w = wRidge(X,y,1.0)\n",
+    "    plt.plot(xt,Xt.dot(w))\n",
+    "    plt.title(\"MSE %f\" % MSE(X.dot(w),y))\n",
+    "    print  MSE(X.dot(w),y)\n",
+    "    \n",
+    "    w = wRidge(X,y,0.1)\n",
+    "    plt.plot(xt,Xt.dot(w))\n",
+    "    plt.title(\"MSE %f\" % MSE(X.dot(w),y))\n",
+    "    print  MSE(X.dot(w),y)\n",
+    "    \n",
+    "    w = wRidge(X,y,10.0)\n",
+    "    plt.plot(xt,Xt.dot(w))\n",
+    "    plt.title(\"MSE %f\" % MSE(X.dot(w),y))\n",
+    "    print  MSE(X.dot(w),y)\n",
+    "    \n",
+    "    plt.plot(xt,yt)\n",
+    "  #plt.plot(x,y)\n",
+    "plot(1)"
+   ]
+  },
+  {
    "cell_type": "markdown",
    "metadata": {},
    "source": [