umz21/wyk/01_Wrpowadzenie.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Uczenie maszynowe 2020/2021\n",
    "### 3 marca 2021\n",
    "# 1. Wprowadzenie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "https://eduwiki.wmi.amu.edu.pl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 1.1. Czym jest uczenie maszynowe?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img style=\"margin: auto\" src=\"terms-cloud.png\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Sztuczna inteligencja (*artificial intelligence*)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* Naśladowanie ludzkich procesów poznawczych za pomocą komputerów"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* Konstruowanie systemów (maszyn), których działanie podobne jest do przejawów ludzkiej inteligencji"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* Dziedzina nauki, która zajmuje się naśladowaniem ludzkiej inteligencji przez komputery"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* Obejmuje m.in. logikę rozmytą, algorytmy ewolucyjne, robotykę i uczenie maszynowe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img style=\"margin: auto\" src=\"venn-ai.png\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Uczenie maszynowe (*machine learning*)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* Tworzenie systemów, które potrafią doskonalić się przy pomocy zgromadzonego doświadczenia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img style=\"margin: auto\" src=\"venn-ai-ml.png\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Sieci neuronowe (neural networks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* Rodzaj struktur matematycznych, które wykonują obliczenia przy pomocy elementów zwanych _sztucznymi neuronami_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* Budowa sieci neuronowych i zasady działania sztucznych neuronów były luźno inspirowane działaniem neuronów w mózgu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img style=\"margin: auto\" src=\"venn-ai-ml-nn.png\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Uczenie głębokie (deep learning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* Użycie sieci neuronowych do automatycznego wydobywania cech z surowych danych"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img style=\"margin: auto\" src=\"venn-ai-ml-nn-dl.png\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Data science"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* Dziedzina nauki zajmująca się przetwarzaniem danych w celu wydobycia z nich wiedzy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img style=\"margin: auto\" src=\"venn-ai-ml-nn-dl-ds.png\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Uczenie maszynowe – klasyczne definicje"
   ]
  },
  {
   "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": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "outputs": [],
   "source": [
    "import IPython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
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  {
   "cell_type": "markdown",
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    }
   },
   "source": [
    "## 1.2. Zastosowania uczenia maszynowego"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true,
    "slideshow": {
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   },
   "outputs": [
    {
     "data": {
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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"600\"\n",
       "            src=\"https://www.youtube.com/embed/ahRcGObyEZo\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x13c6c739d30>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IPython.display.YouTubeVideo('ahRcGObyEZo', width=800, height=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": "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
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"600\"\n",
       "            src=\"https://www.youtube.com/embed/oLTNtvIHJ7M\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x13c6c83d070>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IPython.display.YouTubeVideo('oLTNtvIHJ7M', width=800, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* rozpoznawanie i rozumienie mowy\n",
    "* rozpoznawanie obrazów\n",
    "* tłumaczenie maszynowe\n",
    "* 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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Co potrafi uczenie maszynowe?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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/Lu56xVlZ40M\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x13c6c83d580>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IPython.display.YouTubeVideo('Lu56xVlZ40M', width=800, height=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": "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
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"600\"\n",
       "            src=\"https://www.youtube.com/embed/SWoravHhsUU\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x13c6c83d700>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IPython.display.YouTubeVideo('SWoravHhsUU', width=800, height=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true,
    "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/O8l4Kn-j-5M\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x13c6c83dd60>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IPython.display.YouTubeVideo('O8l4Kn-j-5M', width=800, height=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": "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
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"600\"\n",
       "            src=\"https://www.youtube.com/embed/B1Dk_9k6l08\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x13c6c83db20>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IPython.display.YouTubeVideo('B1Dk_9k6l08', width=800, height=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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/4J0cpdR7qec\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x13c6c83d9a0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IPython.display.YouTubeVideo('4J0cpdR7qec', width=800, height=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": "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
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"600\"\n",
       "            src=\"https://www.youtube.com/embed/Kx-2PyrhnFE\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x13c6c83d490>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IPython.display.YouTubeVideo('Kx-2PyrhnFE', width=800, height=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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   "source": [
    "* https://thispersondoesnotexist.com/\n",
    "* https://thiscatdoesnotexist.com/\n",
    "* https://blog.openai.com/better-language-models/\n",
    "* https://affinelayer.com/pixsrv/"
   ]
  },
  {
   "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..."
   ]
  }
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