2021-03-17 20:09:43 +01:00
{
"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 –
]
},
{
"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": [
{
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"\n",
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" ></iframe>\n",
" "
],
"text/plain": [
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},
"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": [
{
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" ></iframe>\n",
" "
],
"text/plain": [
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},
"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": {
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"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": {
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"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": {
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"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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"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",
"> — <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
"> jeżeli jego skuteczność wykonywania zadania
"> wzrasta z
"\n",
"> — <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
"\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",
"> — 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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"execution_count": null,
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