{ "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": { "image/jpeg": 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F8nEA51ocJsTbyQo6T0Mj6Dmhn3GuUv4tSehiFBzYyNG/GLfkHs7vja+dySQrBDGT4lkp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\nIlc9Vr97bPjNJMFNzYGQzXz0RgC53fG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Yd07FUebjmrlNNXNncwQQzxXYltX+GVAcA+RHgaX93ysm6cs4TeoHj5inNJc2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\nGJKt3s9/n/2qzXJ2e8CseEAO0dPWupbNGxMBjkWUbtjjH4G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UjbiMRZlWRWMYDHByNEoGlsg4I/hUS6V3o+O8IuFBGJ5bds7MBKiKAwH5xPtq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\n3DrEVYBZCoTPdnO+KmXwT3mhupoWikjhlg7ausikTKchj1g3GFcY7tY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OLS7ePoX2aGyn4v6/XBqqb6szoTK/6hP+GfgmQ1lNHC1r5mNcBqCVzrqymfRT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\nRvDKmJ0bjsvxrlHG+V2Vmhte/Ir5TSi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"text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "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": { "image/jpeg": 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"text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "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": [ "\n", "\n", "> Uczenie maszynowe to dziedzina nauki,\n", "> która daje komputerom umiejętność uczenia się\n", "> bez programowania ich _explicite_.\n", "\n", "> — Arthur Samuel, 1959" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "\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", "> — Tom Mitchell, 1998" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "\n", "\n", "Źródło: Drew Conway, http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram" ] }, { "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": [ "\n", "\n", "Źródło: https://recast.ai/blog/machine-learning-algorithms/" ] }, { "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", "> — 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": [] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.3" }, "livereveal": { "start_slideshow_at": "selected", "theme": "amu" } }, "nbformat": 4, "nbformat_minor": 2 }