01_Introduction_to_Data_Driven_Modeling_20160919.ipynb

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    "# Data Driven Modeling\n",
    "<hr style=\"height:3px;border:none;color:stillblue;background-color:black;\" />\n",
    "### PhD seminar series at Chair for Computer Aided Architectural Design (CAAD), ETH Zurich\n",
    "\n",
    "\n",
    "[Vahid Moosavi](https://vahidmoosavi.com/)\n",
    "<hr style=\"height:1px;border:none;color:stillblue;background-color:black;\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a></a>\n",
    "\n",
    "# First Session: Introduction \n",
    "<hr style=\"height:1px;border:none;color:stillblue;background-color:steelblue;\" />\n",
    "<hr style=\"height:1px;border:none;color:stillblue;background-color:black;\" />\n",
    "\n",
    "<span style=\"color:steelblue;font-size: 1em;\"> 20th Semptember 2016</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a></a>\n",
    "<hr style=\"height:3px;border:none;color:stillblue;background-color:black;\" />\n",
    "## The general set up:\n",
    "### What is the purpose of these seminars? \n",
    "### What are the expectations from designers and architects?\n",
    "### How to doemsticate these new computational capacities into the realm of design or how to re-define design?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a></a>\n",
    "<hr style=\"height:3px;border:none;color:stillblue;background-color:black;\" />\n",
    "### What do we mean by modeling? and Why we need to model things? \n",
    "\n",
    "* **generalization**\n",
    "* **formalization**\n",
    "* **control**\n",
    "* **ultimately to communicate**\n",
    "* **what else?**\n",
    "### <a></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A formalism for the modeling process\n",
    "<a></a>\n",
    "![](Images/RosenModel.png)\n",
    "### <a></a>\n",
    "### Main Tasks in Modeling\n",
    "#### Prediction/Classification\n",
    "#### Identification of relationships\n",
    "#### finding important aspects in relation to the target\n",
    "#### pattern recoginition\n",
    "#### etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a></a>\n",
    "### There have been diffrent approaches for the notion of modeling\n",
    "### But different approaches have limits in different levels of complexity\n",
    "![](Images/Model_Coplexity.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## But it has been always like that\n",
    "### Life and death of computational (Urban) modeling concepts\n",
    "![](Images/ngrams3.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### And if we follow the trends ---> Nowadays Big Data! \n",
    "\n",
    "#### <center>Google Trends</center>\n",
    "![](Images/GoogleTrends.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<a></a>\n",
    "## And if we look at the undelrying mechansims\n",
    "#### A historical view to computational modeling and advent of Big Data\n",
    "![](Images/Computational Capacities.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some examples\n",
    "* Analytical\n",
    "* Many of classical physics equations\n",
    "* Newton's Law of Motion\n",
    "    * F=MA \n",
    "\n",
    "![](Images/NewtonSLaw.svg)\n",
    "### <a></a>\n",
    "\n",
    "* Centralized simulation models \n",
    "    * Chaotic systems (Lorenz systems example)\n",
    "    * N-body problems in systems biology\n",
    "* Decentralized simulation models\n",
    "    * Agent Based models in transportations\n",
    "    * Cellular automata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# An Example of unpredictable determinism: Lorenz Systems"
   ]
  },
  {
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   "execution_count": 9,
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   "source": [
    "#Code from: https://jakevdp.github.io/blog/2013/02/16/animating-the-lorentz-system-in-3d/\n",
    "\n",
    "\n",
    "# import numpy as np\n",
    "# from scipy import integrate\n",
    "\n",
    "# # Note: t0 is required for the odeint function, though it's not used here.\n",
    "# def lorentz_deriv((x, y, z), t0, sigma=10., beta=8./3, rho=28.0):\n",
    "#     \"\"\"Compute the time-derivative of a Lorenz system.\"\"\"\n",
    "#     return [sigma * (y - x), x * (rho - z) - y, x * y - beta * z]\n",
    "\n",
    "# x0 = [1, 1, 1]  # starting vector\n",
    "# t = np.linspace(0, 3, 1000)  # one thousand time steps\n",
    "# x_t = integrate.odeint(lorentz_deriv, x0, t)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "from scipy import integrate\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib.colors import cnames\n",
    "from matplotlib import animation\n",
    "%matplotlib inline\n",
    "N_trajectories = 10\n",
    "\n",
    "\n",
    "#dx/dt = sigma(y-x)\n",
    "#dy/dt = x(rho-z)-y\n",
    "#dz/dt = xy-beta*z\n",
    "\n",
    "def lorentz_deriv((x, y, z), t0, sigma=10., beta=8./3, rho=28.0):\n",
    "    \"\"\"Compute the time-derivative of a Lorentz system.\"\"\"\n",
    "    return [sigma * (y - x), x * (rho - z) - y, x * y - beta * z]\n",
    "\n",
    "\n",
    "# Choose random starting points, uniformly distributed from -15 to 15\n",
    "np.random.seed(1)\n",
    "x0 = -15 + 30 * np.random.random((N_trajectories, 3))\n",
    "\n",
    "# Solve for the trajectories\n",
    "t = np.linspace(0, 7, 1000)\n",
    "x_t = np.asarray([integrate.odeint(lorentz_deriv, x0i, t)\n",
    "                  for x0i in x0])\n",
    "\n",
    "# Set up figure & 3D axis for animation\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0, 0, 1, 1], projection='3d')\n",
    "ax.axis('off')\n",
    "plt.set_cmap(plt.cm.YlOrRd_r)\n",
    "plt.set_cmap(plt.cm.hot)\n",
    "# choose a different color for each trajectory\n",
    "colors = plt.cm.jet(np.linspace(0, 1, N_trajectories))\n",
    "\n",
    "# set up lines and points\n",
    "lines = sum([ax.plot([], [], [], '-', c=c)\n",
    "             for c in colors], [])\n",
    "pts = sum([ax.plot([], [], [], 'o', c=c)\n",
    "           for c in colors], [])\n",
    "\n",
    "# prepare the axes limits\n",
    "ax.set_xlim((-25, 25))\n",
    "ax.set_ylim((-35, 35))\n",
    "ax.set_zlim((5, 55))\n",
    "\n",
    "# set point-of-view: specified by (altitude degrees, azimuth degrees)\n",
    "ax.view_init(30, 0)\n",
    "\n",
    "# initialization function: plot the background of each frame\n",
    "def init():\n",
    "    for line, pt in zip(lines, pts):\n",
    "        line.set_data([], [])\n",
    "        line.set_3d_properties([])\n",
    "\n",
    "        pt.set_data([], [])\n",
    "        pt.set_3d_properties([])\n",
    "    return lines + pts\n",
    "\n",
    "# animation function.  This will be called sequentially with the frame number\n",
    "def animate(i):\n",
    "    # we'll step two time-steps per frame.  This leads to nice results.\n",
    "    i = (2 * i) % x_t.shape[1]\n",
    "\n",
    "    for line, pt, xi in zip(lines, pts, x_t):\n",
    "        x, y, z = xi[:i].T\n",
    "        line.set_data(x, y)\n",
    "        line.set_3d_properties(z)\n",
    "\n",
    "        pt.set_data(x[-1:], y[-1:])\n",
    "        pt.set_3d_properties(z[-1:])\n",
    "\n",
    "    ax.view_init(30, 0.3 * i)\n",
    "    fig.canvas.draw()\n",
    "    return lines + pts\n",
    "\n",
    "# instantiate the animator.\n",
    "anim = animation.FuncAnimation(fig, animate, init_func=init,\n",
    "                               frames=500, interval=10, blit=True)\n",
    "\n",
    "# Save as mp4. This requires mplayer or ffmpeg to be installed\n",
    "anim.save('./Images/lorentz_attractor.mp4', fps=15, extra_args=['-vcodec', 'libx264'],dpi=200)\n",
    "\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
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       "\n",
       "<video width=\"600\" height=\"400\" controls>\n",
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       "</video>\n"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
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   "source": [
    "from IPython.display import HTML\n",
    "HTML(\"\"\"\n",
    "<video width=\"600\" height=\"400\" controls>\n",
    "  <source src=\"files/Images/lorentz_attractor.mp4\" type=\"video/mp4\">\n",
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mWtYS9rYGmxlNidw2qyuXUzvbHLUwgOke4QQX3k2v8epUlnlxLU1hXI2A8N0jYD9noz1P\nkt/yjvPMui5wa0k6goqOmZSUzIY72aMydZO0npKzJx5Gs2ayrSfAhmY2m+N/KOoblQw/rSqJcb0c\nJsBslfv6B71LpCV7sFJA7DNPfjDzG7Stak+DQQUVHxJJOIy3mNGs9neql4xshUTPqZnUdK7Dh+2l\nHmcw5z3K5BDHBE2KJuFjRYALFNTx0sLYohZo7zvKzUTx00LpZXYWN1qyRDe5GZJGRML5HBjGi5cT\nYBUTNU1gLoXeC01vtXN4zugHUOcqGVwAZVaQaXSOPkKUZ2PxPPqCw+B1Szh9JStEQz4O/Eb/ANXX\n2K2mSzZVvezRk1OybFQ0zqybbPI/LqcfgFLINISNx1FXHSR7Qxov2n+ymj4aVh4C1LB6bm3e7qOr\nrVR9Xo+CQmNr6yoG3lG/TqCrOSjnNiKnP6V11yNDDRyHjVlXP/w3vPuyW36vov8A0NWT6Rcb+9Ss\nqNLVDbsp4oAdWI4istpNKuzfXtHM1gWXaw3RbLdm1rIh8Dp4+MG6Ri5w957rlBI+M+Q0wW7mVMf/\nAGKn8E0o03FeDzFgRx0swWMcE7do1Ke1jviyeze6SNZ3VD2gVuj4auL04TcjqPwK1ppInuwaOrnM\neNdPPc9x4wUfD08Try001G70ojl2DLuUr2Cri4zYtIRDbk2Rv56leFSMsoSIlGUc5LzRaGkDDxa6\nE051Y74mHr2ddlc4r27CCO1ciF1RFiZSy+Etbyqao4r2jmdt6+1KUtLnDRz+AlbnJSSiw/t0jJXy\n0eRXxRZdRyUpx6PLWjW6B3Id0ej7lC3BUTOkpSaWtGckbxyvxDaOcK5TVrZnmKRphqALmN2vpB2h\nbVVJHVNbiu17M2Pbk5p5iquLRop8SGGqbO/gKhnA1IF8BOvnadqsh5YcMnU7eufIb2ptJtGZ8lUN\nyBP/ACn3qRlRJSOEFeQ5jjZk9sncztx7iqEOF80SVdCXy+E0rxFUgWxW4rxucNvwVelqHB7mMjMc\nrM5aYnVztO0K/nDrzZ7lHV0kdWxrsRZKzOOVvKYeb5K2UsmVjI0lhjrGtngfglHJePcVtTVZc/gJ\n24Jhs2HnCqRyysqeDlDY6vsZUAbRuP5zCnmdBWwG4LXM1kmxjPSmO3dmUnTs8cOuuJfutDK29hxj\nuC47dKFzS1g8IscLH4sLH7+Nb3XUQdVVws3hJozlhivDEOl54zupWwTfgRGalodGo0hFHJwRfeXZ\nFEMb/wC3WqU1fOx2EiKkedTXXmlP9LdXaVLFo/go+DmnbFGf4FM3AOs6z3K5T07KdpbS07IWnWbZ\nlLQj4sm6OZ4LUVR8pTzVA9KrlDGH+huvrVtlFMxgbNWCKMfw6dgjHbmfcr3Bk8uQkc2Srvq6GA/a\nMLhsbxj3I6j5FkpS0RpDRUMTsUVPwknpuu53aVbD5DqhsOdyoy6cpIxfj9Yt71C3T7ZBeKDEN5f8\ngVm6ierNFQqM6h4Y6gwdd0tN6TP8q5R09g5cDOp7v+lbM0614FoWm/8Aige+yjHEfp59M6YMw1ta\neuyGSQa4suY3Vduk4LDhg6G/pjLt1K2x7JG4mODgdoKsmnoyjhJGGSMfkDnu2qrLoyndIZYg6nmP\n8SI4Sekaj1q0+NjxxgCtCHxZtJc3aDrCsm4lbtalR81ZQtxVGGpgbypGDC9o3lu3qWZqdxeK7R7m\n8K4AubfizDYCdh3FXmuDmgtzBXOe12inmSME0Tjd7B/CPpDm3hXTvzJLdLVR1kRcy4IOF7HZFp3E\nKSM4DwZ/pO9Uq+Ihh0jRny7GXOHVK3cfgdiuAtqIWSRm4cA5rlnNb0CVZWkbsQzyIyIW6J3JCIik\nBERAEREAREQBERAYVbl1+8Rs7z/2VkqtR8bhZv5j/dkpRnPNpG1ZI6Ondg5TiGt5iclUljB0lQ0z\nORA10p7MLfeexWao3mp2b33PUFDQDha+uqTqxNiYeZoz7yVWOrZs9EX3EAXOxRwjil7tbs0nzjtv\nICr6UkcygkbH9pJaNnSclG8old2ItG2lM1e8/bHik7GDIdW3rW2jwaiSSufql4sQOxg+evsWukIg\nygioojbhXNiH4dvcCug0BrQALAalJo39zOxch9QJQa+UF0EZtTxjXI7Vfr2dqtaRe5zGUkbrSVBL\nbjzWjlHs96qwtFXUtcwAQQ3jgA2Wyc74DrV9Fcz8WIoxGHVla7HIRYkD/S0bvepJHMp4zXaQsHDk\nM1hnMN5W8Rjkc6sfYQRAiLmG13X+dapUcb9LT+G1QtA0+RYd29Z1JuHdjqyaccXfloZbFV6YAdUY\nqemOqIHN3SUn0lo7RAFPTxcJKMgyMbVBX6Rn0jUOotGuDI2Dys2oALntnio3mn0VFwk2p1Q7Nx+X\nQFEaUYd6ebZslKeW5ei5l2as0zPdz3RaOi2Y7F3Zr9yoSyQ3vUabnlO6NuH3lZGjS8mSunx7xewC\nnjgpW5Q04fzhuXaVpil4L7mMtoow0vLlZL1eb9Cjw+jwbt0jXg77qeKre03ptOuH3Zo7/H4K6Gwh\nnGjjB9EAFQOipn5PpGkbxhuoTqbvYp+tot2cf/ZP3SLUeldJx5TU0NbFtdA4X7P7LaGo0VpGTyMj\nqWpGw8U3XONBS4h4LO6mk2A39xWtS2paLaQp2VcQH2rcnt69axlKnN2ms+vM6YShLOnL8f8Ay/U7\nc/DwgNrovCI26pmZObzpIxlRE2R5NTE3NszMpYuzX+da5VBXVNOzFRzeHU41wPykaObf+cl0aZ1P\nX3qdFy8FOOVGfiFPfpq670RKCbzyfWqN3yjgWjSBE9NrjrGZFp+9bUecZdCtR1UlGWsrHh8J5FSN\nXQ7d06lXilMkzg1ogqzy4nciVaw4qVr/AAeMvpxlNRu5Ud9rd45uxaxmpRus17GMk07SyZ15I45o\n3MkaHscLEHMELnvBoGGKo8vQOFsTszGNzt459ixBMKSFs1M4zaPdnlmYujeObYr0lRGIy64LALlx\nNmgdKSsswpWyZTa92jQMbjLRHVJe5i6d459isPmZC0yMcOCGZJNmjrXCmr/AhelAfRPNgXghrD9w\na3jmCjbSzNjZUOv4Jra+RocYwbnE2O9gOm5CrgbzeRaUcWaOhpGubJGW4eLewmkPBtafu7XHoXOj\njq5Z42VwL8RAiqZ2YWOts4Maj0rtwU7Gls8DeEdh+1kzJHMUqqqiijIqpcZIth1ns2KynCKtbzM4\nYnK0VmQU9NDK61Q189SBYmR2QG9tsh1BSvMlCGid5dTasd+T0rj+HVcjBHAxzo2u8lNIOMwbidvS\np20Ek87RXSvc93JJORXPLabZPM3lsafek7MvTaboafiwAyvOoMGvrUDq/SVTfgo2wDv/AD1KwNGe\nDMxU7GuO1hOtSRV9C2Euc4MwmxaRcg7rBQo1prJWRHbUoPCo5+Jzzo2qqTepqHOHoj8n3K1DoiNj\nbcGCPvXPxt3Kz+si5oNPRVMo/AGf7iE8I0i/kUEbP+JPY9wKstml/J+xZ15vIR6Ojj5LGg8wA+Cm\n8Hd6bv8AMVDwmlP5FKf/AHXfJOH0kzN1FC8bo58+8fFX/T+P3M8bJhA5upzh0PPxWrmSYSHvJbue\n0EKP9aMjaDVwT0w2ue27R0kXAV1j2yMD2ODmnUQbgqHQa3kYmcx9BTE4nQmIj+JA4gdYULqN8F5o\nXcI3+ZEAHt6QMnLsOYDmMjvChLXNeXsyftbscs3eOUjRVGQUekmyhrZS0Odk17eS8/A8yvrlV1K2\nQGqhbcapovTHzCs6NqOFi4Nz8b4wON6TTqK1jLcJRTWJE7wYiXt5PnD4qXWEIuCDqKjgJwYTrabK\n5gsmVNGWhfU0VrCF92D7jsx33HUmivIieiP/AJd9m/gdm34jqWanyGk6acDiygwv97T23HWk44LT\nNK9v8Zj4389swff2rV5+ZJbjykeOgqVRHKcHeFIsY8AjKIisSEREAREQBERAEREBFUPMdPI8a2tJ\nWtI0MpYwPRCzWfuk34D7lmm/do/whTuM/wDk8iCZ9qxptcRxOce75LXQzMGiqe+bnt4Rx3l2Z7yj\nyDJWu9GMN7ifit9GZaMpf+E33KsdG/E2lwJpM5Yx0lVKsGTS1HH5rGvlPSLAe8q2M6g8zQqo42m3\nfcgHe4/JQisN7EnltMRN1injLz0uyHcHK8qFCcWkNIE6xI1o6MAPxKvnJSWlwOPUzubPWzx5yNDK\neH8Rz/5h2LcxCGkjpYXW4QiFp24RrPZdVoCXiheTdzxLVH4f7u5dCJuKvA2U8QA6Xf8A8960/lyM\npbkU9Kky1FNoyEAMcMT7bGjUFppypdTwRaOox5efigDYFJoo+FaQrKsjIOwN6AuWypE1dX6UdmIR\nwcPSQRfs96wopSbqM6WtIrd7kVT5JjdFULr2zlkHnO/OQViKKOiibHG0Oe4ZAbf7KHRsbWQOqZrB\nzhiceb82HarUDHPdjf8AaO37ObqWt8nOXn8fJxbXVvLsY6L7ve3y3IwyF0jgZDjdrGWro3K02KzT\nhH4iNXaVsBhDWtbjLuS30uc8y2nga6JweXOdcDi6mZ7FwV6+aT04FKVC/eevErhxMha0MDfSx3v1\nBbFpIze3r/uFZdM4Oa2NzecNbqUcsMc7ryxvcRtIzXE9pjfT3+Tq/TriQOgaW8ZthvGr5KLgZIs4\nHcXds7PkrjIY2niFzT+I370dHrIz3kCx6wtobapd1vLx69jCexuOcevk5E1NFO7HEPB6oC7XN1O+\na2qaTDTRaQZVllWcHlcgHXIFiB0q9LE2QcYC51HWCqktJCWvbVsjwn+J53551u221KnK3Fa3X5Ra\nltEo9ypu063ctC3T1jNIkUekGCCuaLtcDk7nBUpmex4jqniOoj+yqNhG4rhVPC0zfBJy1zf4E7hm\nxWaB89ZMKbSTyx+G4x54h90DLZrzXThcv3aeXXWZ2SStZ5xfV1+UTTV5ZM6agaLvzkY42ZKdQLdp\nPR1rSKnnqAyrx4qYv1vAJYb5lseoddyrtNCIqoRS2ZVcpk7rku5s/cq0mkY6ScVFMwyTuyngGYcf\nSB3+9aRrQisUPO5j2U5PClfxOzSUsTDwsDcT3jOeQ4nOHyXNqK2m0PUeRcJ43XxRN1xneOncqkQq\nqmnL+FEFC448LTxWg/Bdel0VS0cfCPLQALlzlzyrSm+4rmyp06f/AJHd8DjcLXujc+nBgoXHEWsz\nwjeNtl1KPQ8LY2yi0t8wTndQit8Cc9+joTNRO5T3cVkRvrB2tz2alpJTuphjmmM9ISXSQw3YxnON\npHNdVlRS/wDNI0lUlNWhkdN1VRwERNBkkcPs2NLndY2da5r49IQRObDA2KjcRaKQiR8Y2kC4y5r5\ndyuNqIIo2inwMZazWxDK3uVWpmksS6RkLfSJue9awlhWGEUlxfwebPaYQdr3l4G0scLH/tk0lXEB\n5z8IHS0ZFRy12jgBHDHG0s1cHkR2LmTVWjmuu5xqH9ZC1bpPDcspSQc8zhsoU43wubfLL+zZ0dqq\n08XZW52zO5Fpdj2cZ03MGxk95stxpKHzmVf+RfPjSs7jlSM/+QLduk8/LUrm87c1ODZr5r1b/JTs\nf8hbKK9V8ndZpShLrOnmhP8AiMIHar0LzKwPp6hkzN4OS+fgqYqhvkZSebX3HX1LaONmMSRO4CQ5\nCWI2BO4j5rVbLQl9N0+ZzvaqtOWGtGx9EJy04Zm4fcq0uj+CJn0cRDLe5Z5knMR8QoqHSDppTSVz\nGtmtxXDVJvtuPMrrbwyBhN2O1FZN1NnlaTunv61OyMo1FeIo6ttWx3FMcjDaSN2th3KZ7cQy17FR\nrP2WugrBkx9oZus8U9Ry610FtOKa8GCu04ZwdkvcR+e5cmIPptNvi5MYcCz8Lr3HU4DtXVmNgeaR\np71y9OyNhrqU6nPB/wBJDvgVzweRtTzduJ3VEMqlw3tBUqiH70fwhbs53uK+lBeOnG+oZ71rVf8A\ni1AOaQ9wW2kTx6Qb6hvuK1qf/GaEf4cp/wBi1Wi8yS2/OWPrUijGc/Q1SLGO9hGURFYkIiIAiIgC\nIiAIiICGr/dZfwH3JS/u0X4Qs1IvTyfhPuWtHnSRfgCncZ/8nkV5rNdXDfCD3H5KbR2Wjqb/AITf\ncoKtpM07Wi5fTm3Vf5qbRrmv0bTOabtMTbHqVY6G0tSUfvDvwhVKc30xWc0cQ/3H4q1qqelqrR8T\nTcw/mQMPYT81CIhoxQD9qrzvmH+1qtynDE87mkqrQG1VXRkZtmB6QWhWpml8L2jW5pAVkTLU4lBm\nYx/Lo4mj+on5LoQOwz10uwOAHU0fNUKEhxa4ZXo4j2Eq9Dm/SEf3/e0K0v5GX815FPRDxFoGScbQ\n5/vXCaP/AMep4xrqJXFx5gcPuC7VEL/oxK0Z2jcFxm56I0dbVeS/TiKxp27C3L3O2DtWv4+1y88W\nhijI5RF+rNW4G3Fic3cW/NrP55lVnJ4SLdhd8FbBwxOI1hjyO1TXlkvFv7Hh0ld+S+//AGWIM2Gb\nLFJk3maFHNIwMcXnDENedr7ypnkRxC2QbGFw9PVBgiZFfK1z1WPvdf8ApXm7NQ/VV8D0PVm8Eciv\nU11aJAKcRyMw4S4OAcf6cra9xUfhOk4oI3NpXTyPvcENAbnzWXQ0DSU9Ho06WrWAvObAc8IvbLnK\n5ukK/wDWLXh1FC2HFnxNvOd69urXpbO1Tsml4f2Up7FOurrTxJoNL1cT7V8cEDNuOUXHVmV1qTSV\nNVMDqeYPANsthXC0Zomhr+FY+nbFwduS51ze+88yrPgi0I9klM3wh5c5jvLZix2t3LmnR2Lbm4wy\nnysvcrKNWhNwWdtx9XIXnNjbg8oDX0hRGPGwYyCDqcoKfS8L4GveHB5GbACSo31FS954KLg43+nn\nnvXkQc6EnCW7ebz2WVaN7W5kkkcPBmmqLOjIyJ1N61yTO8x+BOcXyMcDBMw2t810/wBXl5HhUheb\nZD5BaVVPHUUr44mhskdi13u7V106k5u9NdP5Ioyo7P3Ksr/jx45b9MjaKOs0w8x1klnxD7O1utWa\nRkFPBMbMjqKfNwkNg4dK5TKqqmdDXUsZNVA0tmz5TdQy2rqvi4WGKvZI6olZx3FzAB0WUzpRp2qz\nd77jpnKUr03kvb/s1FY6ISTULP2GYgPdM3ixuOsgayN+xbx08FGW+GTCpYCBC6W4azLMAaujasSV\nbpyfB42lsgOMvblfmG1cx9XFRTmF/wC0PDfI8a9h6PNZdTm0v9Yvcs3/AEecpuUnTprFJeh2Za6S\ncWiYHN9OQWb/AJdvXZcWSsgopBE6TwmMZsaDlGd263uVaonqaqPHM9rYDqANm9u3qW8MJbEW4GMD\nhre25PQ35rmnVVJdxW9zso7E5r9+V1wWnrv8jV1XWNa90DeAgJuQBfB1lI4I5nl0rnVDrHkAuz6T\nYJdlO4CocC23k5JTewGzcqjtMU0BcxjnzMHIw5AbxmsMc5Luo7404w+nLl86/cvcCG5GFgt/MnPu\nbksDinitom/+yT8Fzf8A6gbfKlaOl5PyV6k0rFPk+Cw3tkI9+XaQrKNbpmc4xWbj7P3uStfLICG0\n9DUj0WAX77LeF9FLIY3QupJRlxSQB/Scu5b1NKx0RmbeeNhs4EYZIyog01OOmkIklZHwlPMdb27j\n+dxW0HJZXsc0o02r2/D+CSoojG8XLcTuM2VoyeBzc27sViCfhonOkHHYPKDXiaDnfnFwb861ppuG\n0NO4nEYAJGnfbMdoy61pTgM0kWg5EWPUHD3HuXVTlo/J9dZmVSDqU50qmbWaZYr2OdQPcHHh6bjx\nvGvePl1rvwzit0YyobkS3F0Ea1wceCmeTrMDfcSupoJnBaDEexmILfaI4qDueZsUnmifTJDtDTye\ng0Sf5SHfBdALm6VBOixCNcro4+ouF+666Syi701frQ7t5WqM2vA1lzQO1cnTrOF0tQRjXn7wF1jx\npYxvJcepcc3rP0sBHIpW2/Pcuenmm+uszanrc+hUTONO87gApDkFHT/ZYjrdmt2c71RVq/K6Uo4R\nqjxTO6hYd57liA8Ppmok1tp2CJv4jxnd2FYo3NdUVta8jBi4Np3NZr7yVtorEzRrZpR5SYmV1/vG\n4HZYdS0l3USW483vPPZSrSJuFgB17Vuso6BBERWJCIiAIiIAiIgCIiA1e0OYWnURZVtGuJo2Ndym\ncU9StKnT+TrqiPY6zwpWjMpZTi/I3kyroDvY8e5Q6G8nRupzrglfH0C9x3EKat4rGTWzidfq1FQU\nvktMVseyVrJh02wn/aO1VjvRu9EW35TMO+4VaoODS9G702SM9xHuKtT/AGZI83NVNK8WnjqgLmne\nJOrU7uJUb2RD6rGWeT0zKP5sLT1tJHxCvKhWngqujqssAJiceZ1rd4Har6kmW5nBpGmKpZGfOE0P\nW12Jo7CexdCmd+3TX/jRteOq4PwVHSFqOvMpHFxtqG83mP7iCrcnkaiCTzWPMbjzO1d4C03vxMZZ\nNMr6GjwwVdG7zXuFuYrgMuNCYbcakqDiHMbH4FfQOvR6fxX8nVN7HBc2qpxBpyppSLR18d2fiGdv\nf2rnoq8JU9/TO1SwzU+T+Q/NkTr6uL2i3vCswzxSOEYeCXXb03CoUl5aN0DjaRow9BG33HrUtO6S\namihEXBuZhJdcWFjs6xZTWtJX3fJ40oOjUcOGXpmvtY6w8rTt+8yx6Qvnf0na50Ucg1EEdw+Tl9B\nC65LRli4zBz7QqukaRtVTPi1Ai7Tu/PzXBsNdbNtac9GejU79PEtxTMvhX6FQPj/AIdmuHQbKDR9\nXjoW0LWBps7E4i4Ivu35qhoXSX6nrJqHSAPgk+T754Dv/PMrekNFTUThPT3lpjxmSMOrsXd/ldkk\np41o80d2yOnWp9lJ2d7osT0zX08sjW4JYMi9nFBFveuRoqhdXVDKVgyGbng5WB19KoS6bqfCfBpH\nkUodxo2jlc5Osr6z9FaR7qp9bwZZHgwtJFsZv8Piub9JUpOKl/JXNoVsFOfFZFakEFFpSWixudKD\nYixy2i5J3bl2cDZInsHEu3IjWLrh6RdG79LXPBtZ7Wk7zhVN36UvbpQQPprUzXmNwzx6/wA5Ls2v\n/Gzr4ZUVd2uzyY1WptSeR3o3ulgAIvIzJx3fk+9OBPCcK4gNsb33f97FWWsDJtnHNgOkD5KtK4yM\nsACRk5u7OxXDRqOo8NPJaN8+BjtEIweN+hRqnfq/SbKiJowSC0gJyJ+C4lHp3BV8FK0mnMhLeMTh\nJOu21fS10LJaSQPsMTNfOF8zT6OjqpDK1mEtFyMyCehd+y1KVO+NNtrzudNOP6uh3nZRyfLVemh0\n5quWoc9tJdsLja4GbjuCidSsjidEWh8xA4ozEZvkSdp5lYaWxwsijcDPUHqj2AAKGKN0biIyeUM9\nZc++Q6AvObwPCuuR6Cs4XireHHg2TMiqGyNNRGRO5vLNiBbYNgXMr9MMguylAe/bI7UOjermnKio\nkaGiRpEfKa1pGI7de647V842F8mKYscYwc3WyVlRV7yI2WUqkVjaRh3hFZLikc57zkL5ldWh/Rit\nq4mS4WRRv5LpXgX6lHK+TRkNLNSTYXzMLnSNsSDfk82XvXR0dTwVmh2sGj5qivcC0yyucGMF8jc5\naty3UdzOqdRRXc0Kmk/0arNGw8NMyN0WouYb26VUoLRytsSAT2Lvab0tFBoaLRMM3Dva0Nll2ZbA\nvlGy+UxE2aEatKyJTc6N6iz3H2Oi5sdxazTC4OtuGAjsLnBVqV3B1VLIeTDBI88zbgj3LWhliGji\nY5QeEFnkHkNHxPyR7XyHgGttPVWxD0GDUOtZyknLl17HAoZPxN6Fpg0DKMwZrRD3FSUt5a2WRo5D\nbNtvOX/N3LbST2U0cULLEQizedxU1DTmCna0m0jrknn/ALDvK6qaeS36+bOSvWUaVSt/tlHksrm8\n8bZI2wg8WZ4b0MGvuuu5RMLdGRNIs6Ul1t2Ik/FcGCV9UZzDEHQDyTXF1iW+dYdy+gNXCIHVhPkI\n2XBtrVq9WEv2YO70OTZKUoRxSWprN+06XhiHIpm8K/8AEcm/8yuyHi2Gs5Kto2CSKF0tQB4ROccl\nth2DqGS3lcXHCzlOybzbyq15WWFHSlc04VkMUtU7kNFm9AVD9GYHGmlrZB5SpeXZ7tiaVc6eeDRt\nPlfN3MF2IY2wxNjYLNYLBUgvsby7sOfsazm7RGNb8lHW1Ao6R8gFyBZjR5zjkB2reLjvdJs1N6FV\n/fNJ3P2NJ2OkI+A9/Mt4q7uzmWeZXqITDoyn0W115ZxgcRu1vd7+shdMgF7YwOK3O3uVPR/7TNLp\nB3IfxIeZg29Zz6LK7CLgv2uUVHd4SSRZWFlCQiIgCIiAIiIAiIgCIiAwqdR5Otp5RqddjveFcVav\njc+ldg5TbOHUpWpnVV45E8jBJG5jtThYrlF7oqihqX6m4qaU7ibWPaLf1LpwyNmibI05OF1UqKds\nz6mleSGzsxA7jvHcVXSSZrF4o5F8i4soWNEkDoni4thcFHo2odU0bHSC0rbskG5wyKlPElB2OyPS\nksmVfEo00TqvRD6SU2kjBiJ3Eaj7irdBUGqo45SLOIs4bnDIjtVdw8E0mJP4VVxXczxqPWMuoJGT\nSaRdE4+SqTjj5nAcYdevtQ1eY0vE0wNnc3EISS9o2sIs7uz6lBSjh6IwSuu5l4JHX1+i7ssetdY5\niy4DQNG1ro3fYWDXX9A8l39JyPMQr7uRk1dWLNWyWt0WHtyqoDe33hrHWq+k4zpXQ8dXT5VEPHbb\nWCNfuV0uNLU8K7kPs2bp813wUDsWi9Il3/lag/5HLGo+zmqi03l6Uscbb0cZ07XOj0jGAIp+LK30\nHj896s4uClErc2v19Orv96xpKmboyqfLgxaOqvtQM8DthChYTTSeCzlr2Ob5N+x7Vq0vJ9NfBltN\nLtI446r7paPmvujptcCA5rrA54tx3qwDwjTcWcOUBs5wuZHI6B+Fxuw6jv8A7+9XGuFmuacthGz8\n7l5e07Pf8dcTHZ6+HJlHS2iItIMzs2S2ThtXCp6nTH6OuwxnFT35Ds2f2X2Ae1441m32+afksPhB\nycOp3zW2y/5Wps0eyrxxROp0lLvU2fPs/Smjc8SVWhG8MPOaWn3hWZP0tqJ4iKSjEDbWEkjwbdQy\n7SrrtF0zrnwYZ+iB8LKSCggjfiZCWvG3Dn26+9dz/wAlsFrqOfmUcavA5ehtG8A/wypxSTWPGcOM\nbm+rZfUNutXxoShNcyqdTtNTynPubX321K8I23HFzGoDNbSvZSwPfIbWHG+S87aNuq7RLuXS3/Be\nnTw96WpBNaONmwukaAqDuFbTPzJlN7uAtszIC+a/Spul5qqOR7JBEG4mxx3PB57bbV36Hwyno4RX\nA8M7lPxXvlqPPkujslQprNNv8JnNtF5u/WqIapzGxxtp5ceMi7S7ECNh7VFo5woq9zCRhLcQupTw\nc0vCRgA5vt08Ud1yqml2CTBK2LiRXY/mOQW0aXazava35NYz/S0VRfexX+2n3JKYxurp5y4YsLjG\nOfVf39qsUQLm0lRLZxw4n+75qromke+GexAL2B0II1YSbjrxd6Yj4I18dhJTEnCTbE06wuOvSw1F\nFO9tH6/k9KE8cXJq25rhkS1kWKR8jTiD3yY7Zk2dcW/pIPUuPVaQkbAymiIjjsb4TyrldSHFgM+j\n7VFO+2ODFhc0jVY7CFTqmUUp8oTG85kSMLT3Ajsst1PEsnmZqnDEnOOJI59OZ+CBaxj2XyJc353C\nVtfUzMwz1Li30OExLd9FTE3E7HczcR9wUsWi78ZsbiN7uKO/5KVWdO13kjqlad3bN+GfqctzeEID\nCMO07FcGi3SQXjc27cy4mzei67ejo4qSYzOAexrLF7WZM69q2bIaueR1FFkTcPfk1mWu29RtFftW\n6kUlc5aVScJ9i02lvKOjqSShYeGAfLKQY4BzaifkumxwoBIXvxVTheaT0ObpPd2KEzx0rXGnkxyO\n5dUc78zRt6VtSUJmtJUNwRNzaw5kned5VIwbd3r1r8E1ZxwOc3aPHj4L5N6KF1RKKuYYWD7Nu7nV\n7A6pe+BnFY1t5ng8huxo5ysgSzzeD09hIBdzjmIRvO88yvQQRNZ4PGD4NGbvJOcruddbkqMbvX28\nTx25bXUVSStFZJGsOj7QAxyGIy6mNAsOcbslPTwtqZmRRj9ipDYf4jx8B7+hHvlqp3U9O7C63lpR\n/CG4fePcuhGyOmhZDC0Na0Wa0LnpRUL1pKzfXqzubyUeBtK8NBF7ZXJ3BVZJm0sD6iQWcRZjT3Bb\nttITI8gRNzufOO/oXPjvpivL3A+CQ5AHziqK8ni37jSEd703k+hqZw4StmN5Z8xcamq/KSSI263a\nzuC2keGMJWjbQxullcBlic46gt0rKyMaknORFXVHglN5JodK7iRM9J2xU5oeBpYNFxPJknvwj9uH\nW93Xe3WsslaA/StUCGgYYI7ZgHm3uy7lPRwyQsfU1IBqprYgNTRsaOhbO0F11kQWcI4sLBZjRmBs\nGwKYLWNuFueZOsrZZRW9gyiIrEhERAEREAREQBERAEREBhLLKICjTfs1U+n8x3Hj+IUtXG57A+P7\nSM4m8/Mta6MuiEsf2kRxN594U8UjZYmvacnC6mSxIzpvBLCc+GRsOkw5p8jXNxN5pGjPtb/tXRkZ\njYWrn1NMHl9Ni4PGeEhePMePz71YoKo1MB4RoZNGcErL8l3y29ar9SzNZIzPC2to3RPyxDWPNI29\nqrNxaR0e6N54OqiNiR5rxqPRt6Crr/JvxjknlfNVKthpZ/D4gS21p2Acpuw9I9yheIg9xYoqjwqm\na8twPHFe30XDWFHpKk8KhBYG8MwHDi1EHW08xUM/7PJ+sKfjRPA4Zrc8TdjhzjvC6DHtkYHscHNc\nLgjarJ2Ekcagn4WMU0oxODSGY9cjRraecau9WGBkjDQ1N3RvHknnW4bukLTSlA4l1RBiBuHPDddx\nqc3nG7aMlpBUMrWcHKBwhAfxDk8bHsPw2KzSt4Mzd08cTEDjDi0bpGz43CzHnzguVWUh0X+zVWKT\nR7j5KUC5hK7bgyrjFJWkFx+zlGWPnG48yh4Z1J+x6SaJYH5NkIyPSsE3R7ss4s6Yyx96OvWhyRI6\nmtFVFj4njiSjNrwp2OfFxmHGw9ZHz96zVaMn0exzqNoq6B2boHHNvOCqcF3NL9GycMwDjQPyez5r\nZrLiutfk5quzRqd6GT9F5cH4aM6kU7Hi7XAE9hUzXuZk0lvRmOwrkMq6eV5DzwM22+X/AHVlrpox\nxC17eY27jl3rlns8ZK668/k5XKrRlhkrP0/p+TZ0OFzzbE7rwrbhQR9iP/kyVDwstHlGEdII+YWP\nDod8f+YLD9E3pG/Jo0/Vta+zR0PCCBYGNvM03Kifm7hJPMzF8rfLpVUV7TkwtP4Ti9yweGm1jC3e\n/Idm3uW0NmcPqWHn+LFXtEpru5+wlc2cOiJ4j83HVcD3AKF/D1GGJ7wS92FgDbG52noGalOGMSND\nhkAXvd7zuHMqxnMMD645ZcFStdkXOORcunso1Elbl+WWoqSnjlu99yXLViMMFRNK0ANM1m29FoVq\nsije2pYLC7ieg5FUYWvijZTuLXBwwghtsyQd/SrshPhUlhfhGMmYN+VnDu71bsWpyxb/AMWMq1dV\nacalN/S/y/6OGyv/AFS93DcUxOxMG86i3r+RV98dPpQDSGjLyYXESR2sQbbjtVLTNPwkjHkNu0Ax\nPJtwmvvAsotGxz0kkkx4WB5AFxr26x8wuVwi/wBvhvPdUu2j+ppvvO2RLwbG1Mkk8U0Lsg17DYi2\n/YpfCsv/ABK4/wASDEe5W26Vlc3jupp+kWPd8lo/SLNZo6QHeZP7KrpZJZO3j8meKbk24sqCaqa0\nSSSjg90UYDubXrUrIaickx0Ztr4Soffu1BVjPgmEkcrGNHJYxpcGnmupHNq6y2Nj5R6UzrN/y6lr\nVo0nJdnl16FKK2mMG6/q3ZG7xBe9RMa2RvmR5Mb16uxRunlrDwLGtcwaomZMb0+l1q1HooEjwuTH\nuY0WHZrVlpjheIKeK8uyOMXcfgFanQ3rr48jnrbdSh3Y9+Xol5byKmoGxPEk5EkoGrY35K1AyWuP\n7O4MhGRntkOZg2nnVmn0U+Yg1mFzf/TsPFH4nbej3q3VSmJpZCQ0tFnS2yjG4Der44wyh67v7OV0\n6laXabS/IrOZwTBQaPbhcOW698F9pO1xWWB8hFHQHC2PKSa1wz5u/wC5W9PSSywiJhMMJze4cp3M\nD7yujGyGkgEUDWsYwatgWTt9U9Pd8X+Fp+emK39IQQxUcAihblr13JO0krVoNQTn5PafS5hzI1pm\nzdcR8+t3yC5GkdJSVkw0do0BznCznDUB8lV3qO8jaEL9aG9ZUv0lVigojaNucjxqAXXp4YqOmbFG\nMLGD8lRaOoYtHUojabnW951uO9SjyxufsxqG9aqNuZWpUT7sdOszLAZHCR2ockKnKf1lUmAZ0kJ8\nqdkjvR6Bt7N62qZpKqc0dK4tw/bSjzBuHOe5Q4BV/sNKODooeLK9vnfcafeepbRWHMySN4P/ALhU\nird+6wk8APTOov8AgO1XmAvdjOrzQsNa11mMAETMrDaplk3ifgSEWUViQiIgCIiAIiIAiIgCIiAI\niIAiIgNVShPglSYHZRvN4z7wryiqYG1ERY7LaDuO9SnuM5xbzWqMVEXCx2acLwbtduK580phk/WD\nWkFgwVUYzOHYerX0XV2kmc4GKXKVmTufnWKmNzXcPG27gLOb6YVX3Xc0hJTRYBD23Fi0jtUbDwbu\nDdq80/Bc+mlGj3Rx4sVDKbQv/lE+aebd2LqPaHtsUa3oho57T+rJ+DdlRyniHZE47Og7Ez0W83/c\nnG9/5J/6T3dCtkCRjoJ2hwcLEEZOCqMedHuFPUnHSv4scjvN+674FQaJ4jog3FxqXKr9HkOM1OHE\nYsZY3JzXekw7941FSDHoo6nPotlszD82+7o1dBj2vYHscHNcLgjarJ2KtWzRxY6ps0YZVBjmycmQ\nZMkP/I7mVkvMcbo6lpnp9riLuZ+IbelSVej+Ec+WDC17+WxwuyXmcPiqEIkjlEdO7g5Wj91md/sf\nu7epWtllmjO2d1kyZsNTRAS0L/CKY58HfMdBVeWl0bpd3CMcaarb5zcnArbwplPJeUP0fM7WXi8b\n+nZ33Us3AVTcdXABuqIDiHdmFiqbi70n5Gyq3+tWfE5lZR10AIrqRukIv5sfFePmqETKckih0gad\n/wDLmGHqucl3xLU0cXCQ1cNVAPTcAR1qN1boyuY01tMG4hk8t19BCh1Y3/cVmbRcsNo5r1XoygGa\nSjZd7HOHpxta4Hst71llZwTbTPe529zLe66ts0Fo93HoKySnJ9B60fRaTE4p4NLOl9K7MmjnOa1j\nGE9HfmYTVNq1rcrr2IGaRjJN3EG/on5BbB9TUcWnhkcT5xGEfH3hWI9Facja1jNIxta0WAEbch2L\nc6D0hUZVelZHN3NBHxWlorR+hkqFNO7V+bbKUzYaUNOlKgPczNtPHrvz7ukqzQUUuk6gV2kI2spm\nNtFBbIBW6fRGi9G+Ufhc5ueKQ6upRT18ulXml0cDwep82wBZTrRgsMDdJyd/uRwU0QZPURtODhWM\nju4mwxC9rqWWlfLFaEXqaJ7g1vpNOzssekK7UU7KbRrIYxxWPYP9QUOmXOoI3aTisTG0CRh88XsO\ny6mCmks7swkoYmksii3g6iMuiwlpPGidln8CtHtIwtkvZups0eMDoIzUOjdJU+m617Kmm8Hnw3bN\nC8gkbjv610zo+VotDpRoH+JGD7iFvUcacsFTJnGqE13qTOU+Cnc7jMiJ/G9G0dMTlC0nmDz8l0vA\nKq+elIf6YDf/AHrcaKxnytbVSj0WtDR7vis3Ol/t16Gtts0xfdnPEbIRiLGRgbXWb7s0ie6pB8Da\n6oIy8mMI/wAx+a60Gh6WIhzaVhcPPmcZCO1W4aYBpMmbnG5A1KO1jbuxvzyKPZJSadSdzkQ6Mnmy\nqJgxu2Gn+L11KahhpYsEbGxR7Wt29J1lZqa6CkAZrfbKNguSuZHLVaVjL52mlpDk0NN3ydewLKTl\nNOUnkvJf2dUKcKWUEWJ9LwNe6npgZXtGbY/zYdKkoonzRiaqwNbrZG3kjr2qKOighiEULI4YG8sD\nbzX2q60PktgGFvpOHuCxcov6Vfrr8surtm0ktrAXz1AaysYA1vCVBaGtzw3yb81pUT02j4uEldme\ntziuPK6r0vUGGwY1uuM5tj537z93tVlBt55s1jHK7yXWgr9JT6Tl8D0cOK7W7VcbzuC6ujNGwaLp\ni1hu85ySHW4rekpINHQFrNZzc88p5UwY6Q4pBYbG/NbaaFalW/disutTABmN3ZR7BvVepqXyyuo6\nI+VH2kusRD4nmWJqiWrldTUTsIabSz+hzDefcoWDEDQ6M8nGw2ln12O0De7n2LSMbZsySAbc/q2g\nJYxn28wObb52vtce5Xo42Rxtp6doZGwWy2BawQxwRCnpm4WN1nX/ANyrLGhrbAZBZyljyWhIaA0W\nGoLZYWVJIREQBERAEREAREQBERAEREAREQBERAFhZRAVauFxImh+1Zq+8NykgmbPEHt1HZuUpVSW\nB8Uhnpxxjy2el/dTrkZSTi8S8zSohbE2QPjElLL9oy2rnUdLM6klZSzyY4n/ALvMTfF908437Veh\nmZMwPYclTqaZjY3tdHwlK/lx+jzhU+nXQ2TU1dF17Q8WOvYdyjcGyNMNQ0ODhbMZOVKKpfQ4W1Mn\nC0jvs6n0dwd8+1dJzWvbY5gqWt6K8jn+V0Zk7FNRjbrdF8x3oIXU48I0bhkhfxnQA5O52nYe5XMT\nouXm30t3SqrqN8DjLo9zW4s3Qu5D+j0T0KLmkZXLFNVw1TXcG7jNNnMcLOaecLaopoqqPBMwPbs5\nucblSvT6QkAdjpq2MZbHt+Dh3Lfwuoo8q2PFGP48Qy626x3qU7Bx4AsrKUEN/bIfRcQHgdOp3coG\n0miq6UlkMbJxymlmFw6QupHIyWMPje17HanNNwVpUUsFU208TX21E6x0HYr3UtTOzKA0LQ4+PQQk\nk8tosVZdo9hZgbNM1u4uxDvWvgtXB+7VWJv8ucYv9Wvtunhs0X71RyMA8+I8I3uz7lNnuZHMiboG\njDi44y7fe3uWp0KW/Y1s8fNrVuLSVHM7CyoZi9Fxsewq1dYzoxb7yNVUlxOSdF1o5Ok3254wsfqi\nqf8AaaTltuawBddFTsIcCe0l0kcpmgKS+KcyTn/Edl2LpRxsiYGRtDWjUAFpU1MVLFjmeGjYNpO4\nDao6BkoifLOC2SZ2MtJvhGoDsWsaaiskUlNy1Y0kP2J59Eh3YQVFTyNr6aWlq2NMreJNH7j0HWrk\nsbZYnRuF2uFiuZVQODA+op5JHwtOGeB+F1vz0rSNmrGbydyXR2haLRkjpKZjsbhYuc4kgbl0LA6w\nvjdH6S0nPWQwur3hkrrchhI3Z2zX0jaCpP2mk6lw3BrG+4K9WnKMv3HmRTnGavDQvWA1KrLpGlgk\nEcszGvOQbiBJ6lGdEUzzeYzTc0kriOy9lNDBBTDBSwxx78LQLLnnhitTQhnrZw/BT0jnk+e94a34\nnuW0MdS4XqZMR9GMYWjr1qXgH4ieGcL7gFrJE24bd0jz6TslhJ1HHvZdeZZJFaphhIEdhYm7o2Z4\n/wASsRxyuzwhp9J2Z6gpoYWwts0C51neoaivihfwTbyzHVGzM9e5FTbs6j8iVraKJWwtacbiXuHn\nOVOo0kXl0dC1sjm8qVxsxnSdqgrC5wadIyFrX5MpIc3P69vuUsVCZWNdXNZHCzNlM3kt/F6R7lvG\nGXBE3jHN5v7FOlpJa2XhhK4tPKqiLF43MGwc/ZvXYiZFSxtgp4wANTR7ytrvk5AwM37VHUVMFC1o\ndcvebMY0Xc88yss+7FGUpym7klmxgyzOHFFyTkGhUjJNpIeSc6Cj2yanSDm3DnWszMbPCtLObHCw\n3bTg3AOy/pHmW3AzaR49W0w0myA6387+bm7dyukokWNI/wBsY2moPI0LBZ0rcsf3WfF3Yr0UbGRN\nhp2iOJgsLah0LYDGAGjDGN21SgWFgMlk5OfIkNaGiw1LKyisSEREAREQBERAEREAREQBERAEREAR\nEQBERAEREAWFlEBVlhcyThoBxvOb6X91LFK2Vt2npG0KRQywBz8bDgkHnDb0qdTNxcXeJFJTujxO\ngaHMdy4Tqd0blTgdLRi9I109KDnDfykPRfWObs3LoRzHEGSjA/uPQk1OJHCRhwSjU4fHeqZx0NVN\nTM01TDVwiWB4ew7Rs5juWTGWG8fW3YudNDafhQfBKs5cKM2Sczht96mZpHgniLSEfgzybNfe8b+h\n2zoNlNlLQhx4k08MFY0Rzx8YZjY5vOCof22jOX7ZDuyEjfg7uV57WvFiLrS0jNXHG461GaCk1kzn\nxx0dVI91FM6mqfODRhP9TDrUwqK2nyqIBOz+ZDr62n4EqWeCmrAGzRguGq+Th0HWovB62n/d6gTM\n/lzjP/MPiCnIupKRPTV1NVG0MrS4a2HJw6Qc1YXKqJqeQAaTonRkanObjA/qGruW8MGJgdo7SLsA\n81xErfn3qbhxL0sEUzcMsbXjc4XVY6JovMiMX/CeWe4rHDaQh5dLHON8UmE9h+aHSkMZAqGTQH78\nZt2jJWUmt5XAbDR4aLMqalv/ALl/en6vvk6rqXD/AIlvcpYKymqPsZ45PwuBU6nHIhqxVg0fTQP4\nRkd5PTeS53aVaCIobb1BlYRaxuxNud6rfOwK0Gi6KnqDPDTtZIdovl0DZ1K2iXU3uErGHkhvF1rD\nWhosop6yCnF5ZWi+obSqc+kpchFBhxckyaz0N1lYuUcV73LxhKWh0iue/SNPC/goyaic+bGLk/JQ\nDR9VWm9dO4R/y25X7P7q5+x6KpSbNhjGWQzJ95KlKU3pYtaEdXcjEVZVZ1DxTxn+HGbuPS75KKOZ\nudNomJmRs+YjiMP/ADFYIqNJHyrXR038kGxd+I7OgdavR04axrSAGtFgxos0LRJR8WZOo5ZIgpaZ\nkDnOjvPO7J8z9Z/tzBWRGBx5HYiNp1BQz18cMnAQsM84H2UezpOodarzQF8Zn0vMxsLc+BBsxvSf\nO/OSvhbzkUtxJHVstUcGjmhzdRqH8gdHpe7nUIdFRTOjga6s0g8cYk523uOpo5u5SB9VXACnDqSl\n/mFvHcOZp5PSexWKeGKmj4KlYAL5uJvc7ydpSUlFFiGKkwytqa2Th6gcgAWazmaPjrVsML85NWxq\n2ZHhNybuO1brPOX1CwWVhZViQiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAsLKIDSS\nNsjcLxcKA8NB/is/1D5qyiFJRvmtSFksNSwgEOHnNPxChfTPjaWxBssRFjDJq6ipJ6Rkxxi7JBqe\n3WoDNWU32sYmZ6bMj2I4qWhHauH1rIrxRvp3YaCXgiNdLPyf6TrHVccysM0mxjhHWxupJDkMZu09\nDtXxUjZ6WtHBuIJ9B4sUdTytaWsc2WM645fmobkvqRqnGSvFllzGvGYBC0wPbyHZbnLmNp2QG1NL\nJo9/oO40Z6jl2WVgVVbAbVNJwrP5tOb9rTn2XS0ZaEOPEt8KW/aNLefWFBLo+iqTjMLcXps4ru0L\nam0jSVTsEUzeE2scMLh1HNTuiY43tY7xkoaaITa0KhoqiM3p66RoHmytDx8+9amTSkOuGnqB91xY\ne+6t4ZG8l2IbnJwuH7Rpbz6wl+JOPic18kU8gbW6JLbnluDXAdanZQU2ZpZ5Yj9yUkDqNwr7XBwu\nDcLR8Mb83MBO+ytkQ5T3FTDpGnzEkdW0bHDA/tGR7Ap6WrjqWuw3a9hs9jhZzTzqSOLgzk52HcTd\nVNIsMJbXRDykI44Hns2jq1hNCyeLVZl9RNdgBbhJN9gW7XNewOabtIuCFFVzmCHE1uJ5OFjd5VJL\neEm3Y0qKvgi1pHHdyWNzcVEIKqpznk4FvoMzd2qelphAC5xxzP5bzt/soKiXwiUwBxETcpCNbj6I\n+Ko4r+WZbJaepHDFHiLaCNo2OqHZnq3q3FBDTAuvd7uU9xzKyxr8IaxoiYBYDat2xNacVru3lXUb\nFZVHLQq1WkOCeyKGJ0s0nIZqvz8w51uyja+Rs1SBJMBlub0LFRXwQy8E28s9so4xd3Xu61GIa6rz\nnkFLF/LiN3Hpds6u1aqOWZnhvqS1FdBTvEVy+Y6ooxd3Zs61FwNZWZ1D/BYj/CjN3Hpds6u1a8NR\n6PJpqSHhJzmYos3HncTq6SUNHPVjFpGQCP8A9PEeL/U7W7uCnJFjWOoiiBpdE07ZC08ZwNo2nndt\nPRcqSOia2Rs9bJ4ROOTcWa38LfjrVpgAYI4GBjALCwsB0BbsjDTfW7eVm5t/SDXC6TlcVu5SBoAs\nBYLKyoUbEmEWUVgEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAFgrKwg\nK89HDMM2i/Qq/gssOUUkjR904h2FdBFKkzN0o3usig2SocC3yE4Ow3YezNR4JYzeKmlhO3g3tc3s\nK6D42SctoK04FzbcHIQBsOYR2eqC7SOj68ypLE6oiAq6KOoZzgAjqPzULIIWHDS1s9K6/IecQ7HX\n7l0cczeVGHc7SsPlheMErctzxkq95aMv2kf5FYO0pCM2U9UNpaTGew3Hen60ZGP2umqKf8UeIdrb\nhStpo7E00ro/wm47Fs11Ux1nNZK2/KacJHUmLii1k9Gawz0lXxqWojed7HAqdjng2eL/AHgo6igp\nKrOenjed5bn2qD9WviH7HWTw281x4Rv+rPsKnCtxWx0Fq4Aix1KgauspP3ynEkQ1zQXNulmvsurs\nM0c8TZYntfG4XDmm4KmxJiGFkELIoxhYwWaNwVc+W0nnbDTsv/U7+w71cVKKNwkmBGF0ryTn5oyC\noyb2TZtVTSmMinHGJwh3OpaenZTxBjBq1naVJxWN2ABUOHn0ibUjjDTbZ7Zv/CPj2K0YXdyN1iep\nr4qd4iGKWci7Yoxdx+XSVD4PWVmdVJ4PEf4MJzPS/wCSs0tJDSMLYWYb5knMuO8nap1a6WhBUIpt\nG03k4S1l7BsbCS4noUPBVtdnO40kJ/hsN3u6XbOrtXRUWBz+WbD0QquTXMEcEUNLHwVLEABsb8Sp\nRHiN5DiO7YFu0BosBYLKra+chYIiKxJlERAEREAREQBERAEREAREQBERAEREAREQBERAEREAREQB\nERAEREAREQBERAEREAREQBERAYRZRAYSyyiAiMETtbG9i3YwMbhbqWyIQopZpGEWUQkwuc9ngGkY\n3xC0FU7DI0amvtk7rtY9S6ShqadtTGGPJFnNeCN4N/gpQJTqUUPGxSHztXQpHC4IO1UdISOtHR07\nsMs9wHDzGjW74dJUJXZD1uaOvpSZ0f8A5KM2fb+M7d0DbvXSAAFhkAo4IWU8LIom4WMFmjmUilu+\nhIWURQDCLKIDCyiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgC\nIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgMFU6OCTh56qcWkkOFrb3wsGodevrVxFNwZREUAIiIAiI\ngCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIDz/AMZvqj2n6E8Zvqj2n6F5+iA9A8Zvqj2n\n6E8Zvqj2n6F5+iA9A8Zvqj2n6E8Zvqj2n6F5+iA9A8Zvqj2n6E8Zvqj2n6F5+iA9A8Zvqj2n6E8Z\nvqj2n6F5+iA9A8Zvqj2n6E8Zvqj2n6F5+iA9A8Zvqj2n6E8Zvqj2n6F5+iA9A8Zvqj2n6E8Zvqj2\nn6F5+iA9A8Zvqj2n6E8Zvqj2n6F5+iA9A8Zvqj2n6E8Zvqj2n6F5+iA9A8Zvqj2n6E8Zvqj2n6F5\n+iA9A8Zvqj2n6E8Zvqj2n6F5+iA9A8Zvqj2n6E8Zvqj2n6F5+iA9A8Zvqj2n6E8Zvqj2n6F5+iA9\nA8Zvqj2n6E8Zvqj2n6F5+iA9A8Zvqj2n6E8Zvqj2n6F5+iA9A8Zvqj2n6E8Zvqj2n6F5+iA9A8Zv\nqj2n6E8Zvqj2n6F5+iA9A8Zvqj2n6E8Zvqj2n6F5+iA9A8Zvqj2n6E8Zvqj2n6F5+iA9A8Zvqj2n\n6E8Zvqj2n6F5+iA9A8Zvqj2n6E8Zvqj2n6F5+iA9A8Zvqj2n6E8Zvqj2n6F5+iA9A8Zvqj2n6E8Z\nvqj2n6F5+iA9A8Zvqj2n6E8Zvqj2n6F5+iA9A8Zvqj2n6E8Zvqj2n6F5+iA9A8Zvqj2n6E8Zvqj2\nn6F5+iA9A8Zvqj2n6E8Zvqj2n6F5+iA9A8Zvqj2n6E8Zvqj2n6F5+iA9A8Zvqj2n6E8Zvqj2n6F5\n+iAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIg\nCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiA//Z\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"700\"\n",
       "            height=\"600\"\n",
       "            src=\"https://www.youtube.com/embed/JZoGO0MrZPA\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x10f8be090>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('JZoGO0MrZPA',width=700, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# A major shift: \n",
    "# Knowing Vs. Learning (Theory Driven Vs. Data Driven)  Beyond domain expertism\n",
    "\n",
    "### <a></a>\n",
    "## <center>The primary role of Data in Data Driven Modeling</center>\n",
    "### <a></a>\n",
    "![](Images/TheoryDrivenDataDriven.png)\n",
    "### <a></a>\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### <a></a>\n",
    "### <center>Toward a new level of abstraction</center>\n",
    "![](Images/Model_Coplexity.jpg)\n",
    "\n",
    "### <a></a>\n",
    "# <center>Theory Driven models get complicated even with Data</center>\n",
    "### <center>An example in urban transport modeling</center>\n",
    "![](Images/MATSIM.png)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a></a>\n",
    "# <center>But now we have new capacities</center>\n",
    "## An inversion in the role of data in the process of modeling\n",
    "### <a></a>\n",
    "![](Images/InversionLondon.png)\n",
    "### <a></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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yhI27Nud8fPJH0qfDoLtr2MyxXJRTqPhPbpnPbOKrhjvY7O4/AmLOVQZQ5Azk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\nUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUClKUH//2Q==\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"700\"\n",
       "            height=\"600\"\n",
       "            src=\"https://www.youtube.com/embed/0aQxJgHknGs\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x10f8e2990>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('0aQxJgHknGs',width=700, height=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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EGCIiAiIgIiICIiAiIgIiICIiAiIgIiICIiDNERAXSdEh2czYjF/nIZcfK8/y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\nIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIg/9k=\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"700\"\n",
       "            height=\"600\"\n",
       "            src=\"https://www.youtube.com/embed/VQ1f312SVqg\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x10e486cd0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('VQ1f312SVqg',width=700, height=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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gZad+4AgIwYkfAHJnS8M05TQ2jWVM1Ktu27CSp2k9CO+F5HuOZm5SThrGburd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\nVUUkhDjc2eSCcd8YGfcTktonOjr1FZ8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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"700\"\n",
       "            height=\"600\"\n",
       "            src=\"https://www.youtube.com/embed/D6XTyLbO13w\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x10e486e10>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('D6XTyLbO13w',width=700, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## But really how to use this data?\n",
    "#### 1. Info-graphics or just real time visualizations!!\n",
    "#### 2. Data analytics in a real time or a large scale fashion! (80% of Big Data community)\n",
    "#### 3. Looking seriously for universal laws in complex Systems? (Natural models in community of physicists)\n",
    "#### 4. Optimizing the parametric set up of rational models? (rational Models) ---> Very common in engineering thesedays\n",
    "#### 5. Or a new level of data-driven models in coexistence with urban data streams **(Pre-specifics)**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now assuming data is available WHAT should we learn for Data Driven Modeling? \n",
    "\n",
    "![](Images/DataDrivenModelingElements.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# But HOW to learn them?\n",
    "## Resource Based --> Forward but slow and might take a BSc.\n",
    "## Market Oriented --> Backward, might work, but mostly we get simply puzzled\n",
    "## Connecting the dots in a guided path --- Wiki-based approach\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a></a>\n",
    "![](Images/DataDrivenModelingKW.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Topics to be discussed (Not in the order of sessions)  \n",
    "* This list will be updated over time.\n",
    "* All the topics will be discussed with codes in Python\n",
    "\n",
    "**Probability Theory**\n",
    "* Certainty and Determinism\n",
    "* Laplace’s demon\n",
    "* Poncare and the end of determinism\n",
    "* Deterministic Unpredictability (Chaos Theory and Bifurcation)\n",
    "* Uncertainty and Randomness\n",
    "* Fuzziness, vagueness and ambiguity\n",
    "* Variable and Parameter\n",
    "* Random Variable\n",
    "* Probability (Kolmogrov) axioms\n",
    "* Probability distributions\n",
    "* Expected Value\n",
    "* Variance\n",
    "* Covariance\n",
    "* Independent Random Variables \n",
    "* Joint Probability \n",
    "* Baysian Rules and Conditional Probability\n",
    "\n",
    "**Statistics**\n",
    "* Central Limit Theorem (CLT)\n",
    "* Law of large numbers\n",
    "* Statistical Measures \n",
    "    * Mean, Median, Standard Deviation, Skewness, Lp norms\n",
    "* Outliers \n",
    "* histograms\n",
    "* Kernel Density Estimation\n",
    "* Bias Vs. Variance\n",
    "* Accuracy Vs. Precision\n",
    "* Hypothesis Testing\n",
    "* Causality and Correlations\n",
    "* Random Walk\n",
    "* Brownian motion \n",
    "* Resampling\n",
    "* Non-parametric Statistics \n",
    "\n",
    "**Statistical Learning**\n",
    "* Least Square method \n",
    "* Maximum Likelihood Estimation\n",
    "* Regression models and curve fitting using polynomials\n",
    "* Structure Learning in comparison to Polynomials\n",
    "* Regularized Models\n",
    "* Learning densities vs. designed densities\n",
    "* Learning kernels vs. designed Kernels\n",
    "* Learning Dictionaries vs. designed Dictionaries\n",
    "* Over Fitting and generalization\n",
    "* Probabilistic Graphical Models\n",
    "* Quality Measures: Validation, Precision/Recall and other terms\n",
    "* Markov chains and stochastic processes\n",
    "* Baysian Networks\n",
    "\n",
    "**Linear Algebra**\n",
    "* Points,Vectors and Matrices\n",
    "* Matrix operations\n",
    "* Algebraic Operations\n",
    "* Systems of Linear Equations\n",
    "* Similarity and distances between vectors\n",
    "* Euclidean, Hamming, Mahalanobis, Hausdorff\n",
    "* Linear Transformations: PCA, ICA\n",
    "* Markov Chains\n",
    "* Fourier Transformation\n",
    "* Dictionary Learning, Sparse coding\n",
    "\n",
    "\n",
    "**Optimization**\n",
    "* Objective function\n",
    "* Exact Methods\n",
    "* Linear Programming\n",
    "* Complexity\n",
    "* Approximate Methods\n",
    "* Gradient Descent\n",
    "* Hill climbing methods\n",
    "* Meta Heuristics\n",
    "\n",
    "\n",
    "**Machine Learning**\n",
    "* Supervised Learning\n",
    "* Unsupervised Learning\n",
    "* Reinforcement Learning\n",
    "* Classification\n",
    "* Clustering and pattern recognition\n",
    "* Prediction\n",
    "* Function approximation\n",
    "* Feature selection/extraction\n",
    "* Transfer Function\n",
    "* Dimensionality reduction\n",
    "* Manifold learning\n",
    "* Decision trees\n",
    "* Naïve Base classifier\n",
    "* Ensemble methods\n",
    "* Support Vector Machines (SVM)\n",
    "* **Self Organizing Maps (SOM)** \n",
    "* Structured Prediction and Random fields\n",
    "* Kernels and Kernel learning: in Image processing examples \n",
    "* Energy Based Models \n",
    "* Dictionary Learning\n",
    "* Autoencoders\n",
    "* Deep Learning \n",
    "\n"
   ]
  }
 ],
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