diff --git a/is/UB2/ExerciseSheet2.ipynb b/is/UB2/ExerciseSheet2.ipynb
index 13b2642..323c6c0 100644
--- a/is/UB2/ExerciseSheet2.ipynb
+++ b/is/UB2/ExerciseSheet2.ipynb
@@ -80,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 2,
    "metadata": {
     "collapsed": true
    },
@@ -105,7 +105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
@@ -129,7 +129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
@@ -137,8 +137,7 @@
    "source": [
     "def objective(wb):\n",
     "    ### changed ###\n",
-    "    return 1/2*(wb[0]*wb[0]+wb[1]*wb[1])\n",
-    "\n",
+    "    return 0.5*(wb[0]**2+wb[1]**2)\n",
     "def jac(wb):\n",
     "    return hstack((wb[0:2],array([0])))"
    ]
@@ -154,7 +153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 5,
    "metadata": {
     "collapsed": true
    },
@@ -163,7 +162,7 @@
     "def ineq(wb):\n",
     "    #writing \"return a-b\" would encode the constraint a-b >= 0\n",
     "    ### changed ###\n",
-    "    return t_*(dot(wb[0:2],X_.T)+wb[2])-1\n",
+    "    return t*(np.dot(wb[0:2],X.T)+wb[2])-1\n",
     "\n",
     "def grad(wb):\n",
     "    return -1*hstack(((-t*X.T).T, -t[:,newaxis]))"
@@ -178,12 +177,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false,
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-2.  2. -1.]\n"
+     ]
+    }
+   ],
    "source": [
     "def optimizeSVM(X_,t_):\n",
     "    global X,t\n",
@@ -204,7 +211,8 @@
     "    \n",
     "    return wb_opt\n",
     "\n",
-    "wb_opt = optimizeSVM(X_,t_) #the optimized parameters"
+    "wb_opt = optimizeSVM(X_,t_) #the optimized parameters\n",
+    "print(wb_opt)"
    ]
   },
   {
@@ -216,7 +224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
@@ -225,222 +233,164 @@
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       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x106476a90>"
+       "<matplotlib.figure.Figure at 0x7fbcb01f62e8>"
       ]
      },
      "metadata": {},
@@ -474,7 +424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
@@ -503,7 +453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
@@ -514,7 +464,7 @@
        "[-3, 3, -3, 3]"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -522,96 +472,96 @@
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       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x105f235d0>"
+       "<matplotlib.figure.Figure at 0x7f37ddd0c5f8>"
       ]
      },
      "metadata": {},
@@ -627,7 +577,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
@@ -638,7 +588,7 @@
        "[-1, 6, -1, 6]"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -646,78 +596,99 @@
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+       "x0v6sqQvRMRXy65nUBHxM9vfkPRWSfWSy+nHb0j6bds1Saskvcr25yPigyXXlUlEPNn89xnbX1Fj\n",
+       "WfS+cqvqy35J+yPiwebt29UlwMtYQknht/pDkn7F9pm2T5D0+5K+VnJNY8O2JX1O0q6IuL7serKy\n",
+       "fartU5qfnyjpPZJ2lFtVfyLi4xExERFnSfoDSd9OLbxtr7a9tvn5SZIuVqORovIi4oCkedtvaN71\n",
+       "bkmPdnr+sNoIkzrpJyJekvQRSXeqsRP/rxHRcSe4amx/SdK/S3qD7XnbV5ZdU0Zvl/QBNbo3djQ/\n",
+       "UuqqOU3St5tr4A9IuiMivlVyTYNKcTnxtZLua/n+fz0i7iq5piw2Sfqi7R+q0YXy6U5P5EQeAEgU\n",
+       "XSgAkCgCHAASRYADQKIIcABIFAEOAIkiwAEgUQQ4ACSKAAeARP0/TUv6HxMyibsAAAAASUVORK5C\n",
+       "YII=\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x106c8ddd0>"
+       "<matplotlib.figure.Figure at 0x7fbc8e3a6c50>"
       ]
      },
      "metadata": {},
@@ -726,7 +697,9 @@
    ],
    "source": [
     "#X_transformed = 2.5*transpose(vstack((hstack((np.abs(x1),np.abs(x2))),hstack((np.abs(y1),np.abs(y2)))))) ### CHANGE THIS LINE ####\n",
-    "X_transformed = transpose(vstack((hstack((sqrt(x1**2+y1**2),sqrt(x2**2+y2**2))),np.zeros(2*N_half))))\n",
+    "#X_transformed = transpose(vstack((hstack((sqrt(x1**2+y1**2),sqrt(x2**2+y2**2))),np.zeros(2*N_half))))\n",
+    "X_transformed = transpose(vstack((hstack((x1**2,x2**2)),hstack((y1**2,y2**2)))))\n",
+    "\n",
     "### prbly other transormation\n",
     "plt.plot(X_transformed[t_c>0,0],X_transformed[t_c>0,1], 'ro')\n",
     "plt.plot(X_transformed[t_c<0,0],X_transformed[t_c<0,1], 'bo')\n",
@@ -765,31 +738,252 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false,
     "scrolled": true
    },
    "outputs": [
     {
-     "ename": "ValueError",
-     "evalue": "all the input array dimensions except for the concatenation axis must match exactly",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-46-6c4875b82fa6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mwb_opt_c\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptimizeSVM\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_transformed\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mt_c\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;31m#plotSVM(X_transformed,t_c,wb_opt_c)  ### PROVIDE THIS PLOT ###s\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m#plotSVM(X_transformed,t_c,[-2,2,-1])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-22-49e7f2f9b2de>\u001b[0m in \u001b[0;36moptimizeSVM\u001b[0;34m(X_, t_)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m     opt_result = optimize.minimize(objective, wb0, jac=jac,constraints=cons, #the optimizer\n\u001b[0;32m---> 15\u001b[0;31m                                      method='SLSQP', options=opt)\n\u001b[0m\u001b[1;32m     16\u001b[0m     \u001b[0mwb_opt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopt_result\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"x\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m#result of the optimization\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Users/Maximus/anaconda/lib/python2.7/site-packages/scipy/optimize/_minimize.pyc\u001b[0m in \u001b[0;36mminimize\u001b[0;34m(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)\u001b[0m\n\u001b[1;32m    433\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'slsqp'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    434\u001b[0m         return _minimize_slsqp(fun, x0, args, jac, bounds,\n\u001b[0;32m--> 435\u001b[0;31m                                constraints, callback=callback, **options)\n\u001b[0m\u001b[1;32m    436\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'dogleg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    437\u001b[0m         return _minimize_dogleg(fun, x0, args, jac, hess,\n",
-      "\u001b[0;32m/Users/Maximus/anaconda/lib/python2.7/site-packages/scipy/optimize/slsqp.pyc\u001b[0m in \u001b[0;36m_minimize_slsqp\u001b[0;34m(func, x0, args, jac, bounds, constraints, maxiter, ftol, iprint, disp, eps, callback, **unknown_options)\u001b[0m\n\u001b[1;32m    402\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    403\u001b[0m                 \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma_eq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma_ieq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 404\u001b[0;31m             \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mla\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;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    405\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    406\u001b[0m         \u001b[0;31m# Call SLSQP\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: all the input array dimensions except for the concatenation axis must match exactly"
-     ]
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+       "m/n1TMJfkBUru6r8zOaeDr8osBDz7jlVDUkrA3XANYRDkatd1a4xk53Y3MyuBTCz7/ONKMmqxDRC\n",
+       "b/ppvZWAaTkfv5P5nIukcLDweoQLi6ojqZOkZ4EPgbEWphVWmwsIFWlRc1rKxICHJE2WNKTSi8lj\n",
+       "VWC6pOskPS3pakldCj25ZBt4LTT9tGSNVarafyytSZKWBEYCR2auxKuOmf2QSaGsDGxRbXM8JO0A\n",
+       "fGThFKGqvbLNsamZrQf0I6TONq/0guazMLA+cJmZrQ98RTj8uuCTS8LMtm3q65mmnzpg61K9Z2s1\n",
+       "t8Yq9i7QLefjboSrcFckhWPM7gRuMrN7Kr2e5pjZF5L+TeiabKjwcnJtAvxeUh3QGfiRpBvMbN8K\n",
+       "rysvM3s/89/pku4mpCfHVXZVjbwDvGNmkzIfj6SJDbxcVSjZpp+dmmr6qSLVdiUxGfiFpO6SFgV2\n",
+       "A/5V4TXVLIVZ2yOAl83swkqvpxBJy0paKvPrxYFtgajzMkvNzE42s25mtiphQNYj1bp5S+oiqWvm\n",
+       "10sQDmyuqoopM/sAmCZpzcyntgFeKvT8cuXALwaWBB7MlO9cVqb3bTFJu0iaRqhK+Lek0ZVeU5aZ\n",
+       "fQ8cDtxPuNN/m5kVvDNdKZJuJcyZXlPSNEkHVHpNBWwK7E2o6ngm86jG6pkVgEcyOfCngHvN7OEK\n",
+       "r6k51ZzuWw4Yl/P9vM9KcFBGGzgCuFnSc4QqlLMKPdEbeZxzrkb5mZjOOVejfAN3zrka5Ru4c87V\n",
+       "KN/AnXOuRvkG7pxzNco3cOecq1G+gTvnXI3yDdw552rU/wMZBWk9iGk/UAAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fbc8e3a6a58>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
     "wb_opt_c = optimizeSVM(X_transformed,t_c)\n",
-    "#plotSVM(X_transformed,t_c,wb_opt_c)  ### PROVIDE THIS PLOT ###s\n",
-    "#plotSVM(X_transformed,t_c,[-2,2,-1]) "
+    "plotSVM(X_transformed,t_c,wb_opt_c)  ### PROVIDE THIS PLOT ###"
    ]
   },
   {
@@ -798,33 +992,6 @@
    "source": [
     "Feel free (not mandatory!) to rewrite the function plotSVM to plot the data and the separation curve, not in the transformed space (as above), but in the original space."
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -846,21 +1013,21 @@
   },
   "celltoolbar": "Edit Metadata",
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.9"
+   "pygments_lexer": "ipython3",
+   "version": "3.4.3"
   }
  },
  "nbformat": 4,
diff --git a/is/UB2/ISUB2.pdf b/is/UB2/ISUB2.pdf
index 5488770..09ab0bd 100644
--- a/is/UB2/ISUB2.pdf
+++ b/is/UB2/ISUB2.pdf
Binary files differ
diff --git a/is/UB2/ISUB2.tex b/is/UB2/ISUB2.tex
index 9c2eaed..32cd13f 100644
--- a/is/UB2/ISUB2.tex
+++ b/is/UB2/ISUB2.tex
@@ -85,25 +85,30 @@
         Assume:
         $P(M=0)=0.7,P(M=1)=0.2,P(M=2)=0.1;P(m)=0.2, P(e)=0.5$, mice are also afraid of monsters, so if there is a monster under the bed there is no mouse.\\
         $P(n|M=1)=0.7,P(n|M=2)=0.8,P(n|M=0)=0.1, P(n|m)=0.2, P(n|e)=0.1$\\
-        $\Rightarrow P(n)=0.36$\\
+        $\Rightarrow P(n)=0.29$\\
         $\hat{M}^{MAP}=\arg\max\limits_M P(M|n=1)$\\
-        $P(M=0|n=1)=\frac{P(n|M=0)\cdot P(M=0)}{P(n)}=\frac{0.1\cdot 0.7}{0.36}\approx 0.194\\
-        P(M=1|n=1)=\frac{P(n|M=1)\cdot P(M=1)}{P(n)}=\frac{0.7\cdot 0.2}{0.36}\approx 0.389\\
-        P(M=2|n=1)=\frac{P(n|M=2)\cdot P(M=2)}{P(n)}=\frac{0.8\cdot 0.1}{0.36}\approx 0.222$\\
+        $P(M=0|n=1)=\frac{P(n|M=0)\cdot P(M=0)}{P(n)}=\frac{0.1\cdot 0.7}{0.29}\approx 0.241\\
+        P(M=1|n=1)=\frac{P(n|M=1)\cdot P(M=1)}{P(n)}=\frac{0.7\cdot 0.2}{0.29}\approx 0.483\\
+        P(M=2|n=1)=\frac{P(n|M=2)\cdot P(M=2)}{P(n)}=\frac{0.8\cdot 0.1}{0.29}\approx 0.276$\\
         $\hat{M}^{MAP}=1$, so I estimate there is one monster under my bed.\\\\
     \Aufgabe{SVM}{50}
         Changed lines:\\
         \begin{lstlisting}
             def objective(wb):
-                return 1/2*(wb[0]*wb[0]+wb[1]*wb[1])
+                return 0.5*(wb[0]**2+wb[1]**2)
         \end{lstlisting}
         \begin{lstlisting}
             def ineq(wb):
-                return t_*
-                    (list(map(lambda x:np.dot(wb[0:2].T,x),X_))
-                        +wb[2])-1
+                return t*(np.dot(wb[0:2],X.T)+wb[2])-1
         \end{lstlisting}
-        \includegraphics[width=.4\textwidth]{plot1.png}
-        %TODO: wtf?
+        \includegraphics[width=.4\textwidth]{plot1.png}\pagebreak\\
+        \underline{circles:}\\
+            We use that $x^2+y^2=r^2$ where $r$ is the radius:\\
+        \begin{lstlisting}
+    X_transformed = transpose(vstack((hstack((x1**2,x2**2)),
+                                    hstack((y1**2,y2**2)))))
+        \end{lstlisting}
+        \includegraphics[width=.4\textwidth]{plot2.png}
+        \includegraphics[width=.4\textwidth]{plot3.png}\\
 \end{document}
 
diff --git a/is/UB2/plot2.png b/is/UB2/plot2.png
new file mode 100644
index 0000000..ae7367f
--- /dev/null
+++ b/is/UB2/plot2.png
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diff --git a/is/UB2/plot3.png b/is/UB2/plot3.png
new file mode 100644
index 0000000..aaab040
--- /dev/null
+++ b/is/UB2/plot3.png
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