2021-03-02 08:32:40 +01:00
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"source": [
2021-03-03 08:03:43 +01:00
"## Uczenie maszynowe 2020/2021\n",
"### 3 marca 2021\n",
2021-03-02 08:32:40 +01:00
"# 1. Wprowadzenie"
]
},
{
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"metadata": {
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},
"source": [
"## 1.1. Czym jest uczenie maszynowe?"
]
},
{
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}
},
"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": {
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"slide_type": "slide"
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},
"source": [
"### Uczenie maszynowe –
]
},
{
"cell_type": "markdown",
"metadata": {
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"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": "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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"## 1.2. Zastosowania uczenia maszynowego"
]
},
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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": {
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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": {
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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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"\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": {
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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": {
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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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"\n",
" <iframe\n",
" width=\"800\"\n",
" height=\"600\"\n",
" src=\"https://www.youtube.com/embed/fN-bQddbbUI\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
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"IPython.display.YouTubeVideo('fN-bQddbbUI', width=800, height=600)"
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{
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"metadata": {
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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
"\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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