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     "source": [
      "O que \u00e9 Machine Learning?"
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     "level": 2,
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     "source": [
      "OCR - Optical Character Recognition"
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     "metadata": {},
     "source": [
      "<img src=\"Slides/Images/reverse_centaur.jpg\" width=\"600\" align=\"left\"/> <h1 style='color:red;font-size:50px'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Nobody likes <br />&nbsp;&nbsp;Reverse Centaur</h1>"
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     "source": [
      "NLP - Natural Language Processing"
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    {
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     "metadata": {},
     "source": [
      "<img src=\"Slides/Images/cobain_suicide_letter.jpg\" width=\"500\" align=\"left\"/> "
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     "cell_type": "heading",
     "level": 2,
     "metadata": {
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     "source": [
      "Facial recognition"
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    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<img src=\"Slides/Images/facial_recognition.jpg\" width=\"500\" align=\"left\"/>"
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     "source": [
      "Accelerometer Biometric Competition"
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     "source": [
      "<img src=\"Slides/Images/accele_compet.jpg\" align=\"left\" width=\"500\"/><a href=\"http://www.kaggle.com/c/accelerometer-biometric-competition\">P\u00e1gina do Kaggle</a>"
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      "Outros"
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     "source": [
      "- Speech Recognition\n",
      "- Autonomous vehicles\n",
      "- Play chess\n",
      "- Medical diagnosis\n",
      "- Classifyind DNA sequences\n",
      "- Software Engineering metrics\n",
      "- Recommender systems"
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    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {
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     "source": [
      "Defini\u00e7\u00e3o"
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     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "O que h\u00e1 em comum entre esses problemas? Alguns pontos:\n",
      "\n",
      "- Dif\u00edceis(imposs\u00edveis?) de formalizar\n",
      "- Se formaliz\u00e1vel, invi\u00e1vel computacionalmente - chess\n",
      "- Humanos aprendem atrav\u00e9s da experi\u00eancia\n",
      "\n",
      "**Machine Learning**, Tom Mitchell\n",
      "\n",
      "> **Definition**: *A computer program is said to **learn** from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.*\n",
      "\n",
      "Machine Learning, ent\u00e3o, \u00e9 a \u00e1rea que estuda os sistemas que aprendem, de acordo com a defini\u00e7\u00e3o acima."
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       "slide_type": "slide"
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     "source": [
      "- Supervised Learning\n",
      "    - Classification\n",
      "    - Regression\n",
      "    <img src=\"Slides/Images/super_learn.png\" width=\"500\" />"
     ]
    },
    {
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     "metadata": {
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     "source": [
      "- Unsupervised Learning\n",
      "    - Clustering\n",
      "    <img src=\"Slides/Images/unsuper_learn.png\" width=\"500\" />"
     ]
    },
    {
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     "source": [
      "- Reinforcement Learning\n",
      "- Deep Learning\n",
      "- outros"
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     "cell_type": "heading",
     "level": 1,
     "metadata": {
      "slideshow": {
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     "source": [
      "Processo de desenvolvimento"
     ]
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     "metadata": {},
     "source": [
      "Question -> Input data -> Features -> Algorithm -> Parameters -> Evaluation"
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      "Question"
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     "source": [
      "<img src=\"Slides/Images/great_idea.png\" width=\"500\" align=\"left\"/>"
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     "source": [
      "Input Data"
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     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<img src=\"Slides/Images/data.jpg\" width=\"500\" align=\"left\" />"
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     "source": [
      "Features"
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     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<img src=\"Slides/Images/vampire.png\" width=\"500\" align=\"left\" />"
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     "cell_type": "heading",
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      "Algorithm, Parameters"
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     "source": [
      "<img src=\"Slides/Images/svm.png\" width=\"500\" align=\"left\" />"
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      "Evaluation"
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      "<img src=\"Slides/Images/exam.jpg\" width=\"400\" align=\"left\" />"
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     "cell_type": "heading",
     "level": 1,
     "metadata": {
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       "slide_type": "slide"
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     "source": [
      "Background"
     ]
    },
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     "metadata": {},
     "source": [
      "- Statistics\n",
      "- Calculus\n",
      "- Linear Algebra"
     ]
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