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    "## Introdução a Machine Learning\n",
    "\n",
    "Nina S. T. Hirata<br>\n",
    "Prof. Associado<br>\n",
    "Depto. de Ciência da Computação<br>\n",
    "Instituto de Matemática e Estatística<br>\n",
    "Universidade de São Paulo<br>\n",
    "\n",
    "<div style=\"float: left;\"><a href=\"http://www.ime.usp.br/~nina\">www.ime.usp.br/~nina</a></div>\n",
    "<div style=\"float: right;\">nina at ime dot usp dot br</div>\n",
    "\n",
    "<br>\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conteúdo\n",
    "\n",
    "1. <a href=\"../html/intro.html\">Introdução</a> (html)\n",
    "   - classificação supervisionada x não-supervisionada\n",
    "   - exemplos\n",
    "   - regressão x classificação\n",
    "   - classificador de Bayes\n",
    "   - função de custo, minimização de função de custo\n",
    "   <br>\n",
    "   <br>Execute o notebook <mark>intro.ipynb</mark><br><br>\n",
    "   \n",
    "2. Conceitos, fundamentos, prática\n",
    "    0. <a href=\"../html/practice_basics.html\">Familiarizar-se com Python</a> (html)\n",
    "<br>Prática: notebook <mark>practice_basics.ipynb</mark><br><br>\n",
    "\n",
    "    1. <a href=\"../html/practice_regression.html\">Regressão linear com gradiente descendente</a> (html)\n",
    "<br>Prática: notebook <mark>practice_regression.ipynb</mark><br><br>\n",
    "\n",
    "    2. <a href=\"../html/classification.html\">Classificação binária</a> (html)\n",
    "<br>Notebook <mark>classification.ipynb</mark>\n",
    "<br>Prática: notebook <mark>practice_classification1.ipynb</mark><br>Prática: notebook <mark>practice_classification2.ipynb</mark><br><br>\n",
    "\n",
    "    3. <a href=\"../html/practice_scikitlearn.html\">Exemplos usando scikit-learn</a> (html)\n",
    "<br>Prática: notebook <mark>practice_scikitlearn.ipynb</mark>\n",
    "<br><br>\n",
    "\n",
    "    4. <a href=\"../html/mais_scikitlearn.html\">Treinamento, validação, teste, validação cruzada ...</a> (html)\n",
    "<br>Prática: notebook <mark>mais_scikitlearn.ipynb</mark>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Arquivos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lista de html na pasta ML/html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classification.html    practice_classification1.html\r\n",
      "intro.html\t       practice_classification2.html\r\n",
      "main.html\t       practice_regression.html\r\n",
      "mais_scikitlearn.html  practice_scikitlearn.html\r\n",
      "practice_basics.html\r\n"
     ]
    }
   ],
   "source": [
    "!ls ../html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lista de Python notebooks na pasta ML/notebooks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classification.ipynb\tpractice_basics.ipynb\r\n",
      "funcoes.py\t\tpractice_classification1.ipynb\r\n",
      "intro.ipynb\t\tpractice_classification2.ipynb\r\n",
      "main.ipynb\t\tpractice_regression.ipynb\r\n",
      "mais_scikitlearn.ipynb\tpractice_scikitlearn.ipynb\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lista de arquivos de dados na pasta ML/data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data1.txt\r\n"
     ]
    }
   ],
   "source": [
    "!ls ../data"
   ]
  }
 ],
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