zuma/w1/zumz181a.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Uczenie maszynowe 2017/2018\n",
    "# 1. Wprowadzenie. Regresja liniowa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 1.1. Uczenie maszynowe – przykłady zastosowań"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Rozpoznawanie i rozumienie mowy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "outputs": [],
   "source": [
    "import IPython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"600\"\n",
       "            src=\"https://www.youtube.com/embed/qGU-SqUTees\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7ffb08679e10>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IPython.display.YouTubeVideo('qGU-SqUTees', width=800, height=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"600\"\n",
       "            src=\"https://www.youtube.com/embed/UlZtr9fjQcU\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7ffb08679bd0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IPython.display.YouTubeVideo('UlZtr9fjQcU', width=800, height=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"600\"\n",
       "            src=\"https://www.youtube.com/embed/P18EdAKuC1U\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7ffb08679b90>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IPython.display.YouTubeVideo('P18EdAKuC1U', width=800, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Rozpoznawanie obrazów"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img style=\"margin: auto\" width=\"60%\" src=\"https://chaosmail.github.io/images/deep-learning/recognition.png\"/>\n",
    "\n",
    "<sub>Źródło: Christoph Körner, https://chaosmail.github.io/deeplearning/2016/10/22/intro-to-deep-learning-for-computer-vision/</sub>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img style=\"margin: auto\" width=\"60%\" src=\"https://chaosmail.github.io/images/deep-learning/classification.png\"/>\n",
    "\n",
    "<sub>Źródło: Christoph Körner, https://chaosmail.github.io/deeplearning/2016/10/22/intro-to-deep-learning-for-computer-vision/</sub>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wCEABALDA4MChAODQ4SERATGCgaGBYWGDEjJR0oOjM9PDkz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\nQQSReApfqKD1aDlOSR
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"600\"\n",
       "            src=\"https://www.youtube.com/embed/acKTCF0sAfc\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7ffb08679e50>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IPython.display.YouTubeVideo('acKTCF0sAfc', width=800, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img style=\"margin: auto\" width=\"60%\" src=\"https://static.techspot.com/images2/news/bigimage/2014/11/2014-11-18-image-10.jpg\"/>\n",
    "\n",
    "<sub>Źródło: https://www.techspot.com/news/58872-google-new-image-recognition-software-can-describe-entire.html</sub>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"600\"\n",
       "            src=\"https://www.youtube.com/embed/8BFzu9m52sc\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7ffb08679290>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IPython.display.YouTubeVideo('8BFzu9m52sc', width=800, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Tłumaczenie maszynowe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wCEAAUDBAoKCgoKCgoJCgoKCgoKCgoKCQkKCgoKCQoKCgoK\nCgoJChwLCgoaCQkJDSENGh0gHx8fCQsgICAeIBweHyABBQUFCAcIDwkJDxoUEhQaFxUXGBcVFRUV\nFRYVFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFP/AABEIAWgB4AMBIgACEQED\nEQH/xAAcAAABBAMBAAAAAAAAAAAAAAAAAwUGBwECBAj/xABWEAACAQMCAgUGCAoFCgQGAwABAgMA\nBBESIQUxBhMiQVEHMmFxgZEIFCNUYqGx1BhCUnKSk5TB0fAzgrLS4RUkQ0RTY3OiwtMWF4OjNGSE\ns8PxJTV0/8QAGwEBAAMBAQEBAAAAAAAAAAAAAAECAwQFBgf/xAA3EQACAQIFAwIEBAMJAQAAAAAA\nAQIDEQQSITFRBRNBFGEicYGRFTKh4QZS8BYjM0JDscHR8aL/2gAMAwEAAhEDEQA/APGVFFFAFFFF\nAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFA\nFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAF\nFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFF\nFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFFFAFFegvwS+OfOuD\n/tF79xo/BK45874P+0Xv3GrZGVzrk8+0V6CHwSuOfOuD/tF79xrI+CTxz51wf9ovfuNMjGdcnnyi\nvQf4JPHPnfB/2i9+40fgkcc+d8H/AGi9+40yMZ0efaxXoI/BK45874P+0Xv3Gj8Evjnzrg/7Re/c\naZGM8eTz7RXoMfBJ45874P8AtF79xo/BI45874P+0Xv3GmRjPHk8+UV6EPwSeOfO+D/tF79xrR/g\nm8cAJ+N8H2Gf/iL3u3+Y0yMZ48nn6ivQX4JfHPnfB/2i9+41j8Evjnzrg/6+9+40yMjPHk8/UV6C\nHwS+OfO+D/tF79xrH4JfHPnfB/1979xpkYzx5PP1FegfwTOOfO+Efr737jR+CXxz51wf9ovfuNMj\nGePJ5+or0Efgl8c+d8H/AGi9+41j8Evjnzrg/wC0Xv3Gp7chnjyefqK9Aj4JfHPnXB/2i9+41k/B\nK45864P+0Xv3GnbkM8eTz7RXoH8Evjnzrg/7Re/caPwTOOfOuD/tF79xp25E51yefqK9A/gmcc+d\n8H/aL37jR+CZxz53wf8AaL37jTtyGePJ5+or0Afgm8c+dcI/aL37jQPgm8b+d8H/AGi9+407chnj\nyef6K9AD4J3G/nXCP1979xrH4J3G/nXCP1979xp25EdyPJQFFX/+Cdxv51wj9fe/cawvwUeNkkfG\nuEbf7+87/wD6GnbkO5HkoGivQH4J3G/nXCP1979xo/BN43864R+0Xv3GnblwO5Hk8/0V6A/BO438\n64R+0Xn3Gsfgocb+dcI/X3n3GnblwO5HkoCir+PwUON/OuEfr7z7jR+Chxv51wj9fefcaduXA7ke\nSgaKv0/BS43864R+vvPuNYPwU+N/OuEfr7z7lTty4HcjyUHRV+fgqca+dcI/X3n3Ksfgqca+dcJ/\nX3n3KnblwO5HkoSir9T4KPGz/rXCP1959xpT8Ezjnzrg/wC0Xv3GnblwM8eTz9RXoA/BN43864R+\nvvfuNH4JvG/nXCP2i9+407cuB3I8nn+ivQH4JvG/nXCP2i9+40H4J3G/nXCP1979xp25cDuR5PP9\nFX/+Cdxv51wj9fe/ca1b4KXGxj/OuE7nH9Peen/5L0U7cuB3I8lBUVfx+Cjxv51wj9fefcawPgpc\nb+dcI/X3n3GnblwO5HkoOsVf34KPG/nXCP1959xo/BQ43864R+vvPuNO3Lgd2PJQNFX9+Chxv51w\nj9fefcaD8FHjfzrhP6+8+5U7cuCO7HkoGir9/BS43864T+vvPuNY/BU41864T+vvPuVO3Lgd2PJQ\ndFX4Pgqca+dcJ/X3n3Kj8FPjXzrhP6+8+5VPblwO7Hk9vAVitjWgrY5jdRWwrA2FbZqbEAvprBoW\nsGpsSaGsUE0ZqSpuKzmks1laEG70hceafTt79v30uxrnn5D85P7QoSKihq1zRQWAk1nNYoNAArIo\nIooLGWNYzWDzoqQbJQawNqwaAzWpratHoDOqsBq1asZoVN3Nag/bWWrFSAzRmtQaDQi4ZpOHzm9n\n2VuK0jPab1L++gFxQTWuaxmpINga0Q1kUItQDDithWtbAipAnitStb1g0QNCtakfvrZjScjUArbH\neurNcdua6zQlMKKwTRUgyKDWKxQgyaSuOX9Zf7QpXNI3R7J9RPuoDdhtWhWlDyrT+FTYqwFZxWBW\n1Aak+oZ238e721lxQwzj159tZNSQJGjFB50E0BgChhWc0ZoQd7Vg1vWpFYWOhg1BNZxWlSDcHatX\nNYJrFAFFYrNCDYCsVig0ANzpGc7qPpfYCf3UqKTkG6+sn/lYfvoWNs1sKxQKEGaDQKKAM0CiipQM\nZ3oNarWSaA3rWtJZQoLMcKASSeQA764eG8YiuHkEROIz38ypJAJ22800uVc0na+o5Gk2NYLVgtUi\n5tRWhatVztnnvy2Hu/nvpYi4rQawawTUkXELjXto08xnVnGnvxjvpYmigiosLmFNJp57fmp9rVuK\nTi89vzU+16mwuLmta2zWmaAzGaFNa1qDU2IubithSatRqpYBTfxbjENvp619Os4Xl2jzIAJ3rovr\nkRo7ncKM4HMnuUeknA9tRSbo4l5C7z7yPqZJNso7Y0shO6r2Ewo7gM5qVbyY13NQbp7+5LOuU5KM\nGUcmGdLbAnGRuMkjPoNayNUZ8nfWfFflGUgSugAbJRk061IxsMkEe2n9zUWKYPEd+jGo1a/jg6Ld\n+XrrrnuFRSzMFUcyxwPrpg4vxqCzhe4uZFihjBZnb6lUc2bOwUbnIqs/Jl0ml45xea8cMtlw9VNr\nb7f0shIhaTGzPhJJD4FYxyFZ1J5UdLnGKvLYv2ysNYDMSuRnTpw2PSG3B9FLTcNXHZY59OCPqG1c\nL35xscfbSkFx6c1hnlucnr6eyOeVCpwf8D6jSZNOM4Vxvse4/wA91Nz7EiuinPMbwqKWxkmk2Gdv\nZWSa1zWhZsxDJ2VP0R9grbNIxeaPRkfokj91ZzUgVBrYUgprcGhFzcGhjWBQeVCbmhFalq3pN6EG\nM70Ma0zvWTQkdM1lGPfWtC1ibG6msZoFFCTUisGlK0ZeY8RQGKzRRU2FjK0VjNYY0AUm3nD81vtT\n+Nb6qSzlh+afrI/u1DAqTWKM0A1AMg0VisUBk0Z2rANYJ2qUQANBNaaqNe9SBg8oF+kdvpfJWUlW\nAyCUVWkYZHLITT/WrTodY9QBHgA9RAT46iuW3541Fqj3lQk626srb8t11D0SyopP6KP9dThMByfG\nNP8AqoeRhX3cZVk/8tor7XZ0lq17601b0Cr2PUFc1sBvWhNZJqLAwxrfNI4rcVJBtmsE0CtCaIg3\nFJqe035qfbJWQaTU9tvzE+2ShIuDWJBWucVlmoAjO1J5o1bUip3NAK5rOqkgaGbnQmw3cfOvq4x+\nO+T+amB/bdD/AFa7GUKBjkAPcByptllzMWwSI4wTgE489m2HqSnK8gZVBAOlsaXwcHKkkZ83UOeP\nRWLxNNVFSb+K17e3JbttxzW0If0Vn6u8uYM7GRpMellDE/ZTj06478StJLgKrFNIAbzcuwQFsblQ\nTnHqps4XZSLxGWYgCLBUEsOeiNeXPGzV3dOuEm9s7i1WURmePR1gAfSCwLbZ71BH9apq1o5W0zj6\nLgsRCD7kHbNK114vp9
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"600\"\n",
       "            src=\"https://www.youtube.com/embed/06olHmcJjS0\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7ffb085d9850>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IPython.display.YouTubeVideo('06olHmcJjS0', width=800, height=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"600\"\n",
       "            src=\"https://www.youtube.com/embed/rek3jjbYRLo\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7ffb085d9b90>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IPython.display.YouTubeVideo('rek3jjbYRLo', width=800, height=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wCEABALDA4MChAODQ4SERATGCgaGBYWGDEjJR0oOjM9PDkz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\nHpauzR+0KlF27f4fqm
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"600\"\n",
       "            src=\"https://www.youtube.com/embed/_GdSC1Z1Kzs\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7ffb085d9d90>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IPython.display.YouTubeVideo('_GdSC1Z1Kzs', width=800, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Inne zastosowania"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* systemy rekomendacyjne\n",
    "* detekcja spamu\n",
    "* klasyfikacja dokumentów/obrazów\n",
    "* analiza nastrojów\n",
    "* rozpoznawanie pisma odręcznego\n",
    "* samochody autonomiczne\n",
    "* przewidywanie kursów giełdowych\n",
    "* automatyczna diagnostyka medyczna\n",
    "* analiza genów\n",
    "* sztuczna inteligencja w grach\n",
    "* ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"600\"\n",
       "            src=\"https://www.youtube.com/embed/SUbqykXVx0A\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7ffb08679590>"
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     "execution_count": 12,
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     "output_type": "execute_result"
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   ],
   "source": [
    "IPython.display.YouTubeVideo('SUbqykXVx0A', width=800, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 1.2. Czym jest uczenie maszynowe?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img style=\"float: right;\" src=\"https://upload.wikimedia.org/wikipedia/commons/f/f8/This_is_the_photo_of_Arthur_Samuel.jpg\"/>\n",
    "\n",
    "> Uczenie maszynowe to dziedzina nauki,\n",
    "> która daje komputerom umiejętność uczenia się\n",
    "> bez programowania ich _explicite_.\n",
    "\n",
    "> &mdash; <cite>Arthur Samuel, 1959</cite>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img style=\"float: right;\" width=\"20%\" src=\"http://mediad.publicbroadcasting.net/p/wamc/files/styles/x_large/public/201401/tom_mitchell.jpg\"/>\n",
    "\n",
    "> Mówimy, że program komputerowy **uczy się**\n",
    "> z doświadczenia E w odniesieniu do zadania T i miary skuteczności P,\n",
    "> jeżeli jego skuteczność wykonywania zadania T mierzona według P\n",
    "> wzrasta z doświadczeniem E.\n",
    "\n",
    "> &mdash; <cite>Tom Mitchell, 1998</cite>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img style=\"margin: auto\" width=\"40%\" src=\"https://static1.squarespace.com/static/5150aec6e4b0e340ec52710a/t/51525c33e4b0b3e0d10f77ab/1364352052403/Data_Science_VD.png\"/>\n",
    "\n",
    "<sub>Źródło: Drew Conway, http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram</sub>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Uczenie maszynowe to:\n",
    "\n",
    "* doskonalenie działania dla pewnych zadań na podstawie doświadczenia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* tworzenie systemów, które doskonalą swoje działania na podstawie przeszłych doświadczeń"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* zestaw metod, które potrafią w sposób automatyczny wykrywać wzorce w danych, a następnie używać wcześniej niezaobserwowanych wzorców do przewidywania przyszłych zjawisk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Charakterystyczne cechy uczenia maszynowego:\n",
    "\n",
    "* „automatyzacja automatyzacji”\n",
    "* komputer „sam się programuje”\n",
    "* modelowanie danych zastępuje pisanie programu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img style=\"margin: auto\" width=\"80%\" src=\"https://recast.ai/blog/wp-content/uploads/2017/02/image20.png\"/>\n",
    "\n",
    "<sub>Źródło: https://recast.ai/blog/machine-learning-algorithms/</sub>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 1.3. Metody uczenia maszynowego"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Z jakimi rodzajami zadań mamy do czynienia?\n",
    "\n",
    "* Uczenie nadzorowane\n",
    "  * Regresja\n",
    "  * Klasyfikacja\n",
    "* Uczenie nienadzorowane\n",
    "  * Klastrowanie\n",
    "* Uczenie przez wzmacnianie\n",
    "* Systemy rekomendacyjne"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Klasyfikator\n",
    "\n",
    "* Klasyfikator to funkcja $h$, która przykładowi $x$ przyporządkowuje prognozowaną wartość $h(x)$.\n",
    "* Jeżeli funkcja $h$ jest ciągła, to mówimy o zagadnieniu **regresji**.\n",
    "* Jeżeli funkcja $h$ jest dyskretna, to mówimy o zagadnieniu **klasyfikacji**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Algorytm uczący\n",
    "\n",
    "* Dane są przykładowe obserwacje $(X, y)$.\n",
    "* Staramy się dobrać funkcję (klasyfikator) $h$ tak, żeby $h(x) \\sim y$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "W jaki sposób można określić, czy klasyfikator jest „dobry”?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Podział metod uczenia maszynowego\n",
    "\n",
    "> \\[Każdy algorytm uczenia maszynowego\\] stanowi kombinację dokładnie trzech składników.\n",
    "> Te składniki to:\n",
    "> * reprezentacja\n",
    "> * ewaluacja\n",
    "> * optymalizacja\n",
    "\n",
    "> &mdash; Pedro Domingos, “A Few Useful Things to Know about Machine Learning”"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Reprezentacja\n",
    "\n",
    "* drzewa decyzyjne\n",
    "* regresja liniowa\n",
    "* regresja logistyczna\n",
    "* naiwny klasyfikator bayesowski\n",
    "* algorytm $k$ najbliższych sąsiadów\n",
    "* sieci neuronowe\n",
    "* maszyny wektorów nośnych\n",
    "* algorytmy genetyczne\n",
    "* ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Ewaluacja\n",
    "\n",
    "* skuteczność (dokładność)\n",
    "* precyzja i pokrycie\n",
    "* błąd średniokwadratowy\n",
    "* _information gain_\n",
    "* _logistic loss_\n",
    "* BLEU\n",
    "* ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Optymalizacja\n",
    "\n",
    "* optymalizacja kombinatoryczna:\n",
    "  * wyszukiwanie zachłanne,\n",
    "  * _beam search_...\n",
    "* optymalizacja ciągła:\n",
    "  * nieograniczona:\n",
    "    * metoda gradientu prostego,\n",
    "    * metoda Newtona...\n",
    "  * ograniczona:\n",
    "    * programowanie liniowe..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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