diff --git a/doc/pub/week37/ipynb/week37.ipynb b/doc/pub/week37/ipynb/week37.ipynb
index 7c0027279..01c2e9c6a 100644
--- a/doc/pub/week37/ipynb/week37.ipynb
+++ b/doc/pub/week37/ipynb/week37.ipynb
@@ -2167,10 +2167,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 3,
    "id": "1a35e97f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Bz51z9YCmwEqP+xMRyRMp23aS+cMcLqld1u8oZ82zAm9mpYAOwBsAzrl059wer/oTEclLv741kQ/H30nP9CS/o5w1L0fwNYBkYJyZLTKz182s+LEbmdlwM1tgZguSk5M9jCMicgY++4xdxUtRu2dnv5OcNS8LfCGgBfCKc645cAC499iNnHNjnHOJzrnE+PjwuEuKiES2jLR0ai38nrWJHYiOCd/rQb0s8ElAknNuXvDxh2QVfBGRkPbrlK+IS91H9GWX+R3lT/GswDvntgGbzKxu8KmLgV+86k9EJK/s/vDTrKtXB/fxO8qf4vVnj1uACWZWGFgHDPO4PxGRP+2xxKtpUet8HikfvmfQgMcF3jm3GEj0sg8Rkby0addBVuxMp0+vi/yO8qfpSlYRkWzWvjyWu759i4trlvY7yp8WvoeHRUQ8UGbiu1z+RxJVKsX5HeVP0wheRCTo0N791F61kM3nd/A7Sp5QgRcRCfr1w+kUzUij6OU9/Y6SJ1TgRUSCDnw2nbToGOr0Dc/lgY+lAi8iErR590GWNG1H0biSfkfJEyrwIiLAlj2p3NFmCEuefcPvKHlGBV5EBPjhl80AdKgTOWti6TRJERGg5m1/5YNde6jzj5/8jpJnNIIXkQIvIy2dWkvnYVWqYGZ+x8kzKvAiUuD9Nu0bzjm0n+ju3fyOkqdU4EWkwNs9eSqZFkWtAVf4HSVPqcCLSIFX5vuZ/Fa9PqUSKvodJU/pIKuIFGi7D6QzpmE3OrWoQd3Tbx5WNIIXkQLt+9928FGji6n8P4P8jpLnNIIXkQJt88fTaXCoME0T4vyOkudU4EWkwHKBAH2evZfW9ZsTHXWt33HynKZoRKTA2vDtPMrv20lml65+R/GECryIFFh/TJoCQNVrrvA3iEc8naIxsw3APiATyHDO6f6sIhIySsz6ig0Vq1O9QW2/o3giP0bwFznnmqm4i0goObh3P7V+Xcq2tp38juIZTdGISIH045YDXHDTOLjjDr+jeMbrAu+AL8zsZzMbfqINzGy4mS0wswXJyckexxERyTJ7zQ4OlipNs9b1/Y7iGa8LfHvnXAugOzDSzI67k61zboxzLtE5lxgfHznrMItIaGv12N3clLKc2Jhov6N4xtMC75zbHPxzOzAZaO1lfyIiubFl8Up6zp/O+bbP7yie8qzAm1lxMyt55HugK7Dcq/5ERHJr0/uTAaj0l94+J/FWrk+TNLP2QG3n3DgziwdKOOfWn+IlFYDJwcXzCwHvOuc+/1NpRUTyQOGvvmRbXHmqtm3udxRP5arAm9mDQCJQFxgHxADvAO1O9hrn3DqgaR5kFBHJM4cPpVFr+XxWXtidilGRfSJhbkfwVwLNgYUAzrktR6ZfRETCyYrFv3K4XHUK9ezhdxTP5fa/r3TnnCPrtMcjc+oiImHn670x9L/2KWreONjvKJ7LbYGfaGb/AeLM7AbgK+A172KJiHhj7ookmlWJo1TRGL+jeC5XBd459zTwIfARWfPwf3fOvehlMBGRvLZ74xbeubsHN677zu8o+SK3B1mLA9845740s7pAXTOLcc4d9jaeiEjeWfv+pyRmpFOtfQu/o+SL3E7RzAaKmFll4HNgMPCmV6FERLzgPv+cPUVLUqvHRX5HyRe5LfDmnDsI9AFecc71BRp6F0tEJG+5QIBqP//A2iZtiI4pGDezy3WBN7O2wEBgWvC5yF3AQUQizpG7NwUu6eJ3lHyT2wI/GrgX+Ng5t8LMagDfeBdLRCRvfb8Hnug4lKqD+vgdJd/k9nPKQSAADDCzQYARPCdeRCQczNgZxR+XD+XeejX9jpJvclvgJwB3krVYWMC7OCIieS91zz7iPp9C4whfXOxYuS3wyc65zzxNIiLikTUfTuOljx5nafcmFKRVy3Nb4B80s9eBr4G0I0865z72JJWISB5K/Ww6hwoVpk7fnn5HyVe5LfDDgHpkrSJ5ZIrGASrwIhLyKv04mzV1m9PknBJ+R8lXuS3wrZxzdT1NIiLigW3Lf6Xa9o382Heg31HyXW5Pk5xjZg08TSIi4oF1E6cAUOkvV/gbxAe5HcG3ARab2Xqy5uANcM65Jp4lExHJA2PPu5CXbh/HhPYt/Y6S73Jb4C/1NIWIiAcOHc7kh7W76NshEYvwuzedSK4KvHNuo9dBRETy2qr3pvDo5Bep2O0Fv6P4wvMVd8wsGlgAbHbO9fK6PxGRI9I+mESP1XOwJtX9juKL/PjMMhpYmQ/9iIgc5QIBqvw4i9UNWxFbwE6PPMLTAm9mCUBP4HUv+xEROdbvcxZy7q6tpHUtuIcQvR7BPwfczSnWrzGz4Wa2wMwWJCcnexxHRAqKre9+BEC1wX19TuIfzwq8mfUCtjvnfj7Vds65Mc65ROdcYnx8vFdxRKSA+WX3YX5s0JaKjev4HcU3Xo7g2wGXm9kG4H2gs5m942F/IiIA7D10mMdrdObbZ8b6HcVXnhV459x9zrkE51x1oD9ZN+0e5FV/IiJHzF3wG4GMDDrXK+93FF8VjBsTikiBUuZ/72HmsoVUfrxgX8KTL5d2Oedm6Rx4EckPmYczqLlgNsn1GlOoUMG+dXTBu3ZXRCLa6o9nUOZACtb7cr+j+E4FXkQiyt73JpIeXYi6w/r5HcV3KvAiEjFcIEDV2V+wsuH5lIgv43cc3+kgq4hEjF+27uWxrjdz/SX1/I4SEjSCF5GIMeOX7fxYoxnN+uucDlCBF5EIUvKl57gyZjdlSxTxO0pI0BSNiESEzQuWccOUV/ixWlm/o4QMjeBFJCJsGvsuAFWvu8bnJKFDBV5EIkLcjGn8llCbc5vV9ztKyFCBF5Gw98cvv1Jn3XKSL+npd5SQogIvImFv0fTvOVA4loS/DvU7SkhRgReRsPdKbG2u/cdUqpzf1O8oIUUFXkTC2oZtKSzZtIfuidX8jhJyVOBFJKxtfeBhZowdSa/apf2OEnJU4EUkrFWa/gmBUnGcW0lrzxxLBV5Ewtb6WfOovm09e6+4yu8oIUkFXkTC1raXXyPDoqg1cpjfUUKSCryIhKWMtHRqfz6ZZc3aU7ZGFb/jhCStRSMiYem7NduZfNH1DOzb3u8oIcuzEbyZxZrZfDNbYmYrzOxhr/oSkYJn4pI/+KFVF1pco6WBT8bLKZo0oLNzrinQDLjUzNp42J+IFBC7N27hvDde5JrzihITrZnmk/HsJ+Oy7A8+jAl+Oa/6E5GCY/W/XuWuWW/R59xov6OENE//6zOzaDNbDGwHvnTOzTvBNsPNbIGZLUhOTvYyjohEABcIUGHSBH6tUpcaF7X1O05I87TAO+cynXPNgASgtZk1OsE2Y5xzic65xPj4eC/jiEgEWDVpOjW2rmP3YJ0aeTr5MnnlnNsDzAQuzY/+RCRypf7reVJiS9D4rhF+Rwl5Xp5FE29mccHviwJdgFVe9ScikW/r7gPs2HOAFb36UzSupN9xQp6X58FXAt4ys2iy/iOZ6Jyb6mF/IhLhJsxP4uU+DzD7zk5+RwkLnhV459xSoLlX+xeRgiXtwEFmzZjPxU3rU6Vscb/jhAWdQCoiYWHpE/9myrNDGBG31+8oYUMFXkRCXubhDCr+5wXWJdSi+WWd/I4TNlTgRSTkLXlhHFWSN5Ey+k4sSmUrt/STEpGQ5gIBznnuaTbFV6HZ6Ov8jhNWVOBFJKTNnfo9VbauZ9uNo4iO0QK4Z0I/LREJWYGA4+G1UOred3j3gd5+xwk7GsGLSMia8e1yVv+xj8F921MotojfccKOCryIhKS0AwdpcnU3Xpgzlp6NK/kdJyypwItISFp469+pvGsrNa7rT1SU+R0nLKnAi0jI+eOXX2n61kssatmJxkP7+h0nbKnAi0jISbr+ZqIDASq89rLfUcKaCryIhJRZs5dTZdlPLBx4E+c2b+B3nLCm0yRFJGTsOZjOXd9to/r945lw2yV+xwl7GsGLSEhwgQCT736avftSeXBIewoX1WmRf5YKvIiEhPn3P8Gwlx/gZVbSqHIpv+NEBBV4EfHdbzNm0+zph1jaqA2dH73D7zgRQwVeRHyVvHodJftdzZ4ScSRMmURUoWi/I0UMFXgR8c2BQ4f5o2cfSqTuZ/+kyZSpkeB3pIiis2hExBep6Zlc//YCDlw4jP9rW57mXdr5HSnieFbgzawK8DZQAXDAGOfc8171JyLh4+DuFN647yXmlW7Gc6Ovpnmzyn5HikhejuAzgDuccwvNrCTws5l96Zz7xcM+RSTE7d64he2dujBi4yrqvvc5XVXcPePZHLxzbqtzbmHw+33ASkC/SZECbP03czjQsjXVkn5j6XOv07VfF78jRbR8OchqZtWB5sC8E7QNN7MFZrYgOTk5P+KIiA/mPfoiFbt1pkh6Khs++JTmo4b5HSnieX6Q1cxKAB8Btzrn9h7b7pwbA4wBSExMdF7nEZH8lbwvjYc+W0GheRspUbMRFaZ+SL1a1f2OVSB4WuDNLIas4j7BOfexl32JSGjJPJzBzw89w/Rl2/iycVdG3XYjdS98gkK6r2q+8fIsGgPeAFY65/7lVT8iElpcIMCSf79D3KMP0vqPDcQ0aMOg1/6PWhVK+h2twPFyDr4dMBjobGaLg189POxPRHyUGXDMHT+FNbWa0OyWIUQHMln4zGs0W/aDirtPPBvBO+e+B3SfLZEIt2/7TqbOX88rv+wjfulqXkzZxbx7HqPFQ3dQRTfK9pWWKhCRMxbIyGTFhE/5qeNlRCdUJvC3v1GuRGH+5+6BVNj2O+c/cT8xKu6+09EOEcm1zXtSWXv/o9T8YBwNd21lX5FiLOt0GYmjbmJgLy01EGpU4EXklFL37GPZ6+/xYlwTvl+7k0fmLqJM+XPZcvt9NBo5lPPjNL8eqlTgReQ4gYxMVk2axoHXxtLghy9onZ5KkREvM+qKS+h4x0SqlCvhd0TJBRV4ETlq484DzJz8LV3vvp4Gu7dxoHBRVlzQleI3XMeYv/TUWu1hRgVepIBLO3CQZc+9wQ9rd/Fs+USKZKbTqFodttx+Hw1GDqF1ad0+L1ypwIsUUFsWruD3fzxL3ekfkngwhcy6rbnrlYFc2bwy5z51pd/xJA+owIsUIJkBx6zV29l/7wNcNmUsFcxY2qIDSTePoNWgPpyvKZiIogIvUgDsXPc7vz7+PE+Wa8UiStKrTG3KDxrBefeNpnmD2n7HE4+owItEKBcIsGrSdA4+/xKN531Nm0AG3a+9jxvuvIUuDboTE63rHCOdCrxIhNl36DCf/LSBtgN6UH/zb+wtUpyFvQZQ6Z5bGX5BC7/jST5SgReJEL9O/5Y1E6dwV9UuHEzP5ImGbdk1bDiN7riRNnHn+B1PfKACLxLGDu5OYfkz/6H0+HHU/n0VlWOK0O+VXvTu2pymCT3IWrVbCioVeJEw45zj5427WfTOp/R/9GZapx1kfcUazLvtIerdNYIHK8X7HVFChAq8SJj4fe5CNr/0OtMtnvEJral0uDjNzu9MyVtGULdPN2pE6aCp5KQCLxKinHP89t9v2TlhEuVnzeC8LWupbFHs6jqA5rffQLeGFSle5C9+x5QQpgIvEkJ2rFnPmq/m8FnFxsxclczLL99Mq61rWF2zMT/e/AA1b76OnnXP8zumhAkVeBGfHDqcyYZ5S0mZNgNbsICKS+ZTNXkTpaKiufWej2hevzI7//Uie9s0oEG1c/2OK2FIBV7EQ4GMTHZv2kLyohXsXbaKzDVrKLxuLc91u4G56cUY+uNH/O/MseyNLcH6uk3ZcvVAyva6lLld2hEdo3+e8ud49jfIzMYCvYDtzrlGXvUjktdcIEBGZoDDDtIPpZOxfTsZqelkpB4iI/UQhw+ksqdceXYXL01G0mbKfjkNtycFt3cv0bt2UXjHdiZcPIjvytaixc/f8O+PH6dscN+ZFsW20hU4r8sBGndoRIuut7L5qds5t3l9muogqeQxL4cIbwIvAW972AcAI+59k3vGPXjc869edhPz6reh/sZfuOf9J45rf/aq21lcsxkt1vzMrZOfO6790QH3s7pKPS5Y8QMjpr56XPv9Qx9hQ/nqXLLoa67/Yuxx7bfe8DTbylTksnlTGTTr/awnnTvaPnzky+wuHke/7ybRd87k414/8LaxpBWOZdjXb9Prp+k5G8248u53ARj53zF0WTozR/PBwkUZcGtWprs+fZ4LV87J0b6rZBmuHfFvAB6a9ASt1i3K0Z5U5lyG3/AsAE9NeJBGm1bmaF9TsSajhjwOwMvj7qHWH+tztC+u1oi7+/8dgDf/cwsJu7flaP+hViJ/u+puAD566QZKH9iTo/2rBu157LLRAMx4eiCxhw/laP+0WRee7nYj5gJ898/ggcZsP9v3Wl/OS52HUOLQAb54bjDHeq19f8a2+wvl9+5gyr+HA2A4YjIPUzgzg390voGxrXpTa8fvfPXGiONef/elo5jYtCtNt6zm0/FZf/fSowqxt1hJUkqVI94Oc2HtctSs3Jkfq8ZQtF4dyjStT4Um9alcLJaHj9ujSN7zrMA752abWXWv9p9d5Upl2FGj7nHPx1cuT4NK51ApoxzJNesf116xSnkaVS5FxbSK/FEn54cMA6pVK09s5TjKH6zMtnpNjnt9rWrlKRMfR7m9Vdja4PhLwBvUKE+VuDKU3lmDzcnZ2oMXnzQ5L55DxUtyTnJtkg60Pu71rWrFkxlTmNit9fg9Y0/ORjPa1yqHGcQkNWRjofQczRmFi9CxTtb50NasKRtKxuRoTy1eks71ymMGmS1bsj4+Lkf7vrhyXFK/AgAHW7VhbZXKOdvjK9G1YUUM2NumHb/trJWjPe3c6nRvXBGA5PYXcTBld472QLU6XNY0a145qUMXklMP5GgvVLMhvYPtay/qQXRmRo72ovWb06dFAhYI8MslvXP8XABKNW7N1S0TKJR2iBXdruJYFVqcT7+WVSiyvxTL11999HlXuDAWU5gGbTpyX5N6FD+YwLwyjxBVJBYrUhgrUpio2Fj6NWvBtedVo4RdwM6H+1IivgxFShSnHFAOuDt7Z0MuPq5/kfxgLtuoJ893nlXgp55qisbMhgPDAapWrdpy48aNnuUREYk0Zvazcy7xRG2+T/o558Y45xKdc4nx8boCT0Qkr/he4EVExBsq8CIiEcqzAm9m7wFzgbpmlmRm13vVl4iIHM/Ls2gGeLVvERE5PU3RiIhEKBV4EZEIpQIvIhKhPL3Q6UyZWTJwtlc6lQN25GGcvKJcZ0a5zoxynZlIzFXNOXfCi4hCqsD/GWa24GRXc/lJuc6Mcp0Z5TozBS2XpmhERCKUCryISISKpAI/xu8AJ6FcZ0a5zoxynZkClSti5uBFRCSnSBrBi4hINirwIiIRKqIKvJk9YmZLzWyxmX1hZiFxK3oze8rMVgWzTTazOL8zAZhZXzNbYWYBM/P11DEzu9TMVpvZb2Z2r59ZsjOzsWa23cyW+50lOzOrYmYzzeyX4O9wtN+ZAMws1szmm9mSYK6QuTuhmUWb2SIzm+p3luzMbIOZLQvWrQV5ue+IKvDAU865Js65ZsBU4O8+5zniS6CRc64JsAa4z+c8RywH+gCz/QxhZtHAy0B3oAEwwMwa+JkpmzeBS/0OcQIZwB3OuQZAG2BkiPzM0oDOzrmmQDPgUjNr42+ko0YDK0+7lT8ucs41y+tz4SOqwDvn9mZ7WBwIiSPIzrkvnHNHbir6I5DgZ54jnHMrnXOr/c4BtAZ+c86tc86lA+8DvU/zmnzhnJsN7PI7x7Gcc1udcwuD3+8jq3BVPvWrvOey7A8+jAl++f7v0MwSgJ7A635nyU8RVeABzOwxM9sEDCR0RvDZXQf81+8QIaYysCnb4yRCoFiFi+C9j5sD83yOAhydClkMbAe+dM6FQq7nyLoXesDnHCfigC/M7OfgParzTNgVeDP7ysyWn+CrN4Bz7gHnXBVgAnBzqOQKbvMAWR+tJ4RSLglfZlYC+Ai49ZhPsL5xzmUGp0kTgNZm1sjPPGbWC9junPvZzxyn0N4514KsKcqRZtYhr3bs2Q0/vOKcuySXm04ApgMPehjnqNPlMrOhQC/gYpePFx+cwc/LT5uBKtkeJwSfk1MwsxiyivsE59zHfuc5lnNuj5nNJOsYhp8HqdsBl5tZDyAWOMfM3nHODfIx01HOuc3BP7eb2WSypizz5LhY2I3gT8XMamd72BtY5VeW7MzsUrI+Hl7unDvod54Q9BNQ28xqmFlhoD8wxedMIc3MDHgDWOmc+5ffeY4ws/gjZ4mZWVGgCz7/O3TO3eecS3DOVSfr79Y3oVLczay4mZU88j3QlTz8zzCiCjzwRHD6YSlZP6iQOHUMeAkoCXwZPBXqVb8DAZjZlWaWBLQFppnZDD9yBA9A3wzMIOtg4UTn3Ao/shwrhO8t3A4YDHQO/p1aHByh+q0SMDP4b/AnsubgQ+q0xBBTAfjezJYA84FpzrnP82rnWqpARCRCRdoIXkREglTgRUQilAq8iEiEUoEXEYlQKvAiIhFKBV5CkpntP/1WJ33tzcFVKZ2Zlcv2vJnZC8G2pWbWIltbpSOrDJpZp7xacdDMZuVmpc7gioLlTrPNV2ZWOi9yScGgAi+R6AfgEmDjMc93B2oHv4YDr2Rrux14LV/Snb3xwAi/Q0j4UIGXkBYcdT8VvIBtmZn1Cz4fZWb/tqx19r80s+lmdjWAc26Rc27DCXbXG3g7uOLhj0CcmVUKtl0FHHeBiZm1NrO5wXXE55hZ3eDzQ83sk2DfG4KfGm4PbvejmZXJtpvBwQuRlptZ6+Dry1rWPQtWmNnrgGXr85PgwlMrjll8agow4Gx/llLwqMBLqOtD1rriTckalT8VLMp9gOpkrR8/mKyrcU/nhKtWmlkNYLdzLu0Er1kFXOica07W6qSPZ2trFMzRCngMOBjcbi5wbbbtigUX3xoBjA0+9yDwvXOuITAZqJpt++uccy2BRGCUmZUFcM7tBooceSxyOmG32JgUOO2B95xzmcAfZvYtWQW1PTDJORcAtgUXtTpblYDkk7SVAt4KrnPkyFrf/IiZwbXY95lZCvBZ8PllQJNs270HWWvLm9k5wbVaOpD1nwPOuWlmtjvb9qPM7Mrg91XImlLaGXy8HTg322ORk9IIXgqSk61amUrWKoMn8ghZhbwRcNkx22Uf8QeyPQ6Qc/B07HogJ10fxMw6kfVJpW3wrkiLjukzNphX5LRU4CXUfQf0s6ybSMSTNfKdT9aB1KuCc/EVgE652NcU4NrgvH4bIMU5t5Ws2yhWP8lrSvH/ly4eepbv4chxg/bBPlPIWg72muDz3YEjZ8eUImu66KCZ1SPrdnwEtzOgIrDhLHNIAaMCL6FuMrAUWAJ8A9ztnNtG1jroScAvwDvAQiAFwMxGBVfJTACWBg9iQtb9AdYBv5F1xswIAOfcAWCtmdU6Qf//BP5hZos4+ynNQ8HXvwocWY3yYaCDma0ga6rm9+DznwOFzGwl8ARZt3g8oiXwY7bbP4qcklaTlLBlZiWcc/uDBx3nA+2Cxf9s9nUl0NI59795GjIPmdnzwBTn3Nd+Z5HwoIOsEs6mBg9YFgYeOdviDuCcmxwGZ6csV3GXM6ERvIhIhNIcvIhIhFKBFxGJUCrwIiIRSgVeRCRCqcCLiESo/wdImTSKTx+UsgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
@@ -2184,7 +2197,7 @@
     "np.random.seed(3155)\n",
     "\n",
     "# Generate the data.\n",
-    "nsamples = 100\n",
+    "nsamples = 1000\n",
     "x = np.random.randn(nsamples)\n",
     "y = 3*x**2 + np.random.randn(nsamples)\n",
     "\n",
@@ -2198,7 +2211,7 @@
     "lambdas = np.logspace(-3, 5, nlambdas)\n",
     "\n",
     "# Initialize a KFold instance\n",
-    "k = 5\n",
+    "k = 10\n",
     "kfold = KFold(n_splits = k)\n",
     "\n",
     "# Perform the cross-validation to estimate MSE\n",
@@ -2378,10 +2391,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
    "id": "a399bb68",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_17573/3517936899.py:63: RuntimeWarning: divide by zero encountered in log10\n",
+      "  plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Common imports\n",
     "import os\n",
@@ -2432,7 +2466,7 @@
     "X[:,0] = 1.0\n",
     "estimated_mse_sklearn = np.zeros(Maxpolydegree)\n",
     "polynomial = np.zeros(Maxpolydegree)\n",
-    "k =5\n",
+    "k =30\n",
     "kfold = KFold(n_splits = k)\n",
     "\n",
     "for polydegree in range(1, Maxpolydegree):\n",
@@ -2451,6 +2485,14 @@
     "plt.legend()\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "482bdaa3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/doc/pub/week38/html/._week38-bs001.html b/doc/pub/week38/html/._week38-bs001.html
index e587e025a..9ff52a9a8 100644
--- a/doc/pub/week38/html/._week38-bs001.html
+++ b/doc/pub/week38/html/._week38-bs001.html
@@ -238,7 +238,7 @@ MathJax.Hub.Config({
 
 <ul>
 <li> Lab Wednesday and Thursday: work on project 1</li>
-<li> Thursday: Summary of regression methods, cross-validation  and discussion of project 1.  Start Logistic Regression</li>
+<li> Thursday: Summary of regression methods, cross-validation  and discussion of project 1.  Start Logistic Regression <a href="https://youtu.be/sdt_BFla8uA" target="_self">Video of lecture</a></li>
 <li> Friday: Classification problems and Logistic Regression, from binary cases to several categories. Start optimization methods</li>
 <li> Reading recommendations:
 <ol type="a"></li>
diff --git a/doc/pub/week38/html/week38-reveal.html b/doc/pub/week38/html/week38-reveal.html
index 42fcc57c7..99995e895 100644
--- a/doc/pub/week38/html/week38-reveal.html
+++ b/doc/pub/week38/html/week38-reveal.html
@@ -199,7 +199,7 @@ MathJax.Hub.Config({
 
 <ul>
 <p><li> Lab Wednesday and Thursday: work on project 1</li>
-<p><li> Thursday: Summary of regression methods, cross-validation  and discussion of project 1.  Start Logistic Regression</li>
+<p><li> Thursday: Summary of regression methods, cross-validation  and discussion of project 1.  Start Logistic Regression <a href="https://youtu.be/sdt_BFla8uA" target="_blank">Video of lecture</a></li>
 <p><li> Friday: Classification problems and Logistic Regression, from binary cases to several categories. Start optimization methods</li>
 <p><li> Reading recommendations:
 <ol type="a"></li>
diff --git a/doc/pub/week38/html/week38-solarized.html b/doc/pub/week38/html/week38-solarized.html
index dc451cefd..36770a048 100644
--- a/doc/pub/week38/html/week38-solarized.html
+++ b/doc/pub/week38/html/week38-solarized.html
@@ -194,7 +194,7 @@ MathJax.Hub.Config({
 
 <ul>
 <li> Lab Wednesday and Thursday: work on project 1</li>
-<li> Thursday: Summary of regression methods, cross-validation  and discussion of project 1.  Start Logistic Regression</li>
+<li> Thursday: Summary of regression methods, cross-validation  and discussion of project 1.  Start Logistic Regression <a href="https://youtu.be/sdt_BFla8uA" target="_blank">Video of lecture</a></li>
 <li> Friday: Classification problems and Logistic Regression, from binary cases to several categories. Start optimization methods</li>
 <li> Reading recommendations:
 <ol type="a"></li>
diff --git a/doc/pub/week38/html/week38.html b/doc/pub/week38/html/week38.html
index b3474b662..f2d9d8ead 100644
--- a/doc/pub/week38/html/week38.html
+++ b/doc/pub/week38/html/week38.html
@@ -271,7 +271,7 @@ MathJax.Hub.Config({
 
 <ul>
 <li> Lab Wednesday and Thursday: work on project 1</li>
-<li> Thursday: Summary of regression methods, cross-validation  and discussion of project 1.  Start Logistic Regression</li>
+<li> Thursday: Summary of regression methods, cross-validation  and discussion of project 1.  Start Logistic Regression <a href="https://youtu.be/sdt_BFla8uA" target="_blank">Video of lecture</a></li>
 <li> Friday: Classification problems and Logistic Regression, from binary cases to several categories. Start optimization methods</li>
 <li> Reading recommendations:
 <ol type="a"></li>
diff --git a/doc/pub/week38/ipynb/ipynb-week38-src.tar.gz b/doc/pub/week38/ipynb/ipynb-week38-src.tar.gz
index f172974bb..828cd95dc 100644
Binary files a/doc/pub/week38/ipynb/ipynb-week38-src.tar.gz and b/doc/pub/week38/ipynb/ipynb-week38-src.tar.gz differ
diff --git a/doc/pub/week38/ipynb/week38.ipynb b/doc/pub/week38/ipynb/week38.ipynb
index 805096efe..b8f3e4450 100644
--- a/doc/pub/week38/ipynb/week38.ipynb
+++ b/doc/pub/week38/ipynb/week38.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "652eaa23",
+   "id": "877dea8a",
    "metadata": {
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@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "412093d8",
+   "id": "4cb5bf43",
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@@ -27,7 +27,7 @@
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-   "id": "56415917",
+   "id": "57dd0b9f",
    "metadata": {
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@@ -36,7 +36,7 @@
     "\n",
     "* Lab Wednesday and Thursday: work on project 1\n",
     "\n",
-    "* Thursday: Summary of regression methods, cross-validation  and discussion of project 1.  Start Logistic Regression\n",
+    "* Thursday: Summary of regression methods, cross-validation  and discussion of project 1.  Start Logistic Regression [Video of lecture](https://youtu.be/sdt_BFla8uA)\n",
     "\n",
     "* Friday: Classification problems and Logistic Regression, from binary cases to several categories. Start optimization methods\n",
     "\n",
@@ -53,7 +53,7 @@
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   {
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-   "id": "0ba9703f",
+   "id": "5ab41e7c",
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@@ -66,7 +66,7 @@
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+   "id": "c93b1367",
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@@ -78,7 +78,7 @@
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+   "id": "c930b886",
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@@ -88,7 +88,7 @@
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-   "id": "864f53c3",
+   "id": "9336ad4e",
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@@ -101,7 +101,7 @@
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+   "id": "300ccd68",
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@@ -111,7 +111,7 @@
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-   "id": "e024e880",
+   "id": "7caed49a",
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@@ -123,7 +123,7 @@
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-   "id": "283f572d",
+   "id": "65a739f2",
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@@ -136,7 +136,7 @@
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   {
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-   "id": "86ea66e3",
+   "id": "c309f271",
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@@ -149,7 +149,7 @@
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   {
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-   "id": "ca159901",
+   "id": "04715c07",
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@@ -161,7 +161,7 @@
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-   "id": "a3abca8c",
+   "id": "6b355785",
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@@ -173,7 +173,7 @@
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-   "id": "bf976ebb",
+   "id": "7e878d37",
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@@ -183,7 +183,7 @@
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@@ -196,7 +196,7 @@
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+   "id": "ccb1e72d",
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@@ -208,7 +208,7 @@
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-   "id": "bc5693e7",
+   "id": "5f462a77",
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@@ -220,7 +220,7 @@
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-   "id": "49a40462",
+   "id": "9a83404f",
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@@ -245,7 +245,7 @@
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-   "id": "2fe8efc8",
+   "id": "83addf19",
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@@ -261,7 +261,7 @@
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-   "id": "f7e0070d",
+   "id": "133d9fd0",
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@@ -277,7 +277,7 @@
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-   "id": "ebee0b5a",
+   "id": "db67f161",
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@@ -291,7 +291,7 @@
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@@ -319,7 +319,7 @@
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@@ -332,7 +332,7 @@
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-   "id": "77d9a00f",
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@@ -434,7 +434,7 @@
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+   "id": "58525535",
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@@ -456,7 +456,7 @@
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+   "id": "fd23a15f",
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@@ -482,7 +482,7 @@
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@@ -506,7 +506,7 @@
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+   "id": "5b334bd9",
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@@ -531,7 +531,7 @@
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@@ -543,7 +543,7 @@
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@@ -561,7 +561,7 @@
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@@ -579,7 +579,7 @@
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@@ -590,7 +590,7 @@
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@@ -617,7 +617,7 @@
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+   "id": "f375e3b0",
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@@ -630,7 +630,7 @@
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    "execution_count": 2,
-   "id": "5e4150c0",
+   "id": "06a5d637",
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@@ -695,7 +695,7 @@
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+   "id": "e1bc5710",
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@@ -708,7 +708,7 @@
   {
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    "execution_count": 3,
-   "id": "45630690",
+   "id": "a96fd8e6",
    "metadata": {
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@@ -727,7 +727,7 @@
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    "cell_type": "markdown",
-   "id": "ab7468b4",
+   "id": "8aadaa8c",
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@@ -738,7 +738,7 @@
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   {
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-   "id": "a6b22ad4",
+   "id": "c1ed1f2d",
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@@ -750,7 +750,7 @@
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   {
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-   "id": "6f7756e6",
+   "id": "dc708799",
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@@ -769,7 +769,7 @@
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   {
    "cell_type": "markdown",
-   "id": "fdf8ff6c",
+   "id": "deb66eaf",
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@@ -791,7 +791,7 @@
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-   "id": "7e8a0b52",
+   "id": "5eab3f55",
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@@ -803,7 +803,7 @@
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-   "id": "2558e7cd",
+   "id": "2cfaf9ba",
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@@ -813,7 +813,7 @@
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-   "id": "0a5940fd",
+   "id": "5602a876",
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@@ -826,7 +826,7 @@
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    "execution_count": 4,
-   "id": "f24a162d",
+   "id": "bebd570c",
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@@ -891,7 +891,7 @@
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-   "id": "e3599eec",
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@@ -903,7 +903,7 @@
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-   "id": "ff260317",
+   "id": "d7a2faec",
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@@ -918,7 +918,7 @@
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+   "id": "d78e34c0",
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@@ -930,7 +930,7 @@
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+   "id": "e6c5c790",
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@@ -942,7 +942,7 @@
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+   "id": "5e4acb48",
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@@ -959,7 +959,7 @@
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+   "id": "5fcb0159",
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@@ -973,7 +973,7 @@
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+   "id": "d3b03302",
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@@ -983,7 +983,7 @@
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-   "id": "08e03785",
+   "id": "c4105ebe",
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@@ -995,7 +995,7 @@
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-   "id": "ec678734",
+   "id": "686ff507",
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@@ -1007,7 +1007,7 @@
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-   "id": "63ce94a6",
+   "id": "3889f853",
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@@ -1019,7 +1019,7 @@
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-   "id": "be19d2fb",
+   "id": "a9eab992",
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@@ -1030,7 +1030,7 @@
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-   "id": "10ad3233",
+   "id": "ca1d0b7c",
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@@ -1042,7 +1042,7 @@
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-   "id": "1bff70de",
+   "id": "b39a640a",
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@@ -1053,7 +1053,7 @@
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+   "id": "c4d33ac9",
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@@ -1069,7 +1069,7 @@
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-   "id": "a130861d",
+   "id": "6fbc0427",
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@@ -1081,7 +1081,7 @@
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+   "id": "5baf7aa8",
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@@ -1091,7 +1091,7 @@
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@@ -1103,7 +1103,7 @@
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+   "id": "d30b5329",
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@@ -1118,7 +1118,7 @@
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@@ -1130,7 +1130,7 @@
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+   "id": "85acea2e",
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@@ -1141,7 +1141,7 @@
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+   "id": "c25ba78b",
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@@ -1153,7 +1153,7 @@
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+   "id": "dab3def5",
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@@ -1165,7 +1165,7 @@
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@@ -1177,7 +1177,7 @@
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@@ -1187,7 +1187,7 @@
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@@ -1199,7 +1199,7 @@
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@@ -1213,7 +1213,7 @@
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@@ -1225,7 +1225,7 @@
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@@ -1235,7 +1235,7 @@
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@@ -1247,7 +1247,7 @@
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@@ -1257,7 +1257,7 @@
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@@ -1269,7 +1269,7 @@
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@@ -1280,7 +1280,7 @@
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@@ -1303,7 +1303,7 @@
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@@ -1315,7 +1315,7 @@
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@@ -1325,7 +1325,7 @@
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@@ -1337,7 +1337,7 @@
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@@ -1354,7 +1354,7 @@
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+   "id": "2e59571f",
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@@ -1364,7 +1364,7 @@
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@@ -1379,7 +1379,7 @@
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-   "id": "08c21e60",
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@@ -1415,7 +1415,7 @@
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+   "id": "8973213d",
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@@ -1429,7 +1429,7 @@
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@@ -1474,7 +1474,7 @@
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@@ -1499,7 +1499,7 @@
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-   "id": "44fdab91",
+   "id": "f09d77e3",
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@@ -1511,7 +1511,7 @@
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@@ -1522,7 +1522,7 @@
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@@ -1534,7 +1534,7 @@
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@@ -1547,7 +1547,7 @@
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@@ -1558,7 +1558,7 @@
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-   "id": "135cad2c",
+   "id": "c8cc320e",
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@@ -1613,7 +1613,7 @@
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@@ -1623,7 +1623,7 @@
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   {
    "cell_type": "markdown",
-   "id": "d29a2c84",
+   "id": "6ed29afd",
    "metadata": {
     "editable": true
    },
@@ -1644,7 +1644,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "94e5b193",
+   "id": "89adf3c9",
    "metadata": {
     "editable": true
    },
@@ -1661,7 +1661,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dcc96426",
+   "id": "05fe738b",
    "metadata": {
     "editable": true
    },
@@ -1676,7 +1676,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ba265702",
+   "id": "9f3c76cf",
    "metadata": {
     "editable": true
    },
@@ -1686,7 +1686,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e3d4f0e1",
+   "id": "d22d494a",
    "metadata": {
     "editable": true
    },
@@ -1702,7 +1702,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6d2ee642",
+   "id": "01fd9d57",
    "metadata": {
     "editable": true
    },
@@ -1714,7 +1714,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d7783361",
+   "id": "7ca1d957",
    "metadata": {
     "editable": true
    },
@@ -1725,7 +1725,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fbe0ae96",
+   "id": "43918ffd",
    "metadata": {
     "editable": true
    },
@@ -1737,7 +1737,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "045b1f3c",
+   "id": "a3dfa9ac",
    "metadata": {
     "editable": true
    },
@@ -1747,7 +1747,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0517345b",
+   "id": "93c4877e",
    "metadata": {
     "editable": true
    },
@@ -1761,7 +1761,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d4213f23",
+   "id": "2e281ee4",
    "metadata": {
     "editable": true
    },
@@ -1773,7 +1773,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "14a27b15",
+   "id": "37f01359",
    "metadata": {
     "editable": true
    },
@@ -1783,7 +1783,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0e81a6a6",
+   "id": "a482d3de",
    "metadata": {
     "editable": true
    },
@@ -1795,7 +1795,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "770eacc6",
+   "id": "de980205",
    "metadata": {
     "editable": true
    },
@@ -1807,7 +1807,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "49e04247",
+   "id": "62ee83b4",
    "metadata": {
     "editable": true
    },
@@ -1827,7 +1827,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "609a75a9",
+   "id": "2f827dcd",
    "metadata": {
     "editable": true
    },
@@ -1843,7 +1843,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "78bb3209",
+   "id": "f8905ede",
    "metadata": {
     "editable": true
    },
@@ -1859,7 +1859,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1049b712",
+   "id": "3aae5fc7",
    "metadata": {
     "editable": true
    },
@@ -1870,7 +1870,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "330b28d9",
+   "id": "1cd60615",
    "metadata": {
     "editable": true
    },
@@ -1882,7 +1882,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1e8c72f3",
+   "id": "0ab2920c",
    "metadata": {
     "editable": true
    },
@@ -1892,7 +1892,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ce030426",
+   "id": "102e22cd",
    "metadata": {
     "editable": true
    },
@@ -1904,7 +1904,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6bf02606",
+   "id": "e6c2e6a9",
    "metadata": {
     "editable": true
    },
@@ -1914,7 +1914,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "450a24ec",
+   "id": "815f26ac",
    "metadata": {
     "editable": true
    },
@@ -1926,7 +1926,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6d4a2e16",
+   "id": "11b8a074",
    "metadata": {
     "editable": true
    },
@@ -1948,7 +1948,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ac343659",
+   "id": "f8b0c0c2",
    "metadata": {
     "editable": true
    },
@@ -1961,7 +1961,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "130a91e2",
+   "id": "7748bb6e",
    "metadata": {
     "editable": true
    },
@@ -1974,7 +1974,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "16340ef0",
+   "id": "9f6a5e2b",
    "metadata": {
     "editable": true
    },
@@ -1984,7 +1984,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d030336e",
+   "id": "08837bac",
    "metadata": {
     "editable": true
    },
@@ -2002,7 +2002,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6ea40e0f",
+   "id": "1c202345",
    "metadata": {
     "editable": true
    },
@@ -2012,7 +2012,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dd21c0e4",
+   "id": "6ff02742",
    "metadata": {
     "editable": true
    },
@@ -2027,7 +2027,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c4491448",
+   "id": "ac23e197",
    "metadata": {
     "editable": true
    },
@@ -2037,7 +2037,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1107e41e",
+   "id": "b287aa32",
    "metadata": {
     "editable": true
    },
@@ -2051,7 +2051,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "29262250",
+   "id": "9ffd750a",
    "metadata": {
     "editable": true
    },
@@ -2061,7 +2061,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1dc2881a",
+   "id": "fbe28370",
    "metadata": {
     "editable": true
    },
@@ -2075,7 +2075,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a6340da5",
+   "id": "41f96bfe",
    "metadata": {
     "editable": true
    },
@@ -2090,7 +2090,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "73719a1f",
+   "id": "5856a602",
    "metadata": {
     "editable": true
    },
@@ -2107,7 +2107,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "525c3e51",
+   "id": "779f8888",
    "metadata": {
     "editable": true
    },
@@ -2119,7 +2119,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ae118f5c",
+   "id": "f11bb7b7",
    "metadata": {
     "editable": true
    },
@@ -2133,7 +2133,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "81f76f58",
+   "id": "1dc6e7fb",
    "metadata": {
     "editable": true
    },
@@ -2148,7 +2148,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "233e197e",
+   "id": "a002ea53",
    "metadata": {
     "editable": true
    },
@@ -2160,7 +2160,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a6822f0e",
+   "id": "9f8dceae",
    "metadata": {
     "editable": true
    },
@@ -2171,7 +2171,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "abfd1571",
+   "id": "6207bafe",
    "metadata": {
     "editable": true
    },
@@ -2199,7 +2199,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "77cf9cd3",
+   "id": "060f1c07",
    "metadata": {
     "editable": true
    },
diff --git a/doc/src/week38/week38.do.txt b/doc/src/week38/week38.do.txt
index bf9457ca1..540917ab2 100644
--- a/doc/src/week38/week38.do.txt
+++ b/doc/src/week38/week38.do.txt
@@ -11,6 +11,7 @@ DATE: September 22 and 23
 
 * Lab Wednesday and Thursday: work on project 1
 * Thursday: Summary of regression methods, cross-validation  and discussion of project 1.  Start Logistic Regression
+  "Video of lecture":"https://youtu.be/sdt_BFla8uA"
 * Friday: Classification problems and Logistic Regression, from binary cases to several categories. Start optimization methods
 
 * Reading recommendations: