Add ml1

ml1/2_8_Conclusions.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](files/images/EscUpmPolit_p.gif \"UPM\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Course Notes for Learning Intelligent Systems"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2016 Carlos A. Iglesias"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## [Introduction to Machine Learning](2_0_0_Intro_ML.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Table of Contents\n",
+    "* [Conclusions](#Conclusions)\n",
+    "* [References](#References)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Conclusions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this chapter, we have introduced the essentials of machine learning in a practical way. \n",
+    "\n",
+    "We have gone through some of the most interesting features offered by scikit-learn. They essentially concern the machine learning features, and the visualisation features brought by the matplotlib and seaborn libraries. In the following session we will analyse other machine learning algorithms, such as SVM and Perceptron.\n",
+    "\n",
+    "Before concluding this session,  we include a comparison of the algorithms  reviewed in this session on synthetic datasets, based on the sample code of [sklearn](http://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html#example-classification-plot-classifier-comparison-py).\n",
+    "\n",
+    "Particularly in high-dimensional spaces, data can more easily be separated linearly and the simplicity of classifiers such as naive Bayes and linear SVMs might lead to better generalization than is achieved by other classifiers.\n",
+    "\n",
+    "The plots show training points in solid colors and testing points semi-transparent. The lower right shows the classification accuracy on the test set.\n",
+    "\n",
+    "The [DummyClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.dummy.DummyClassifier.html#sklearn.dummy.DummyClassifier) is a classifier that makes predictions using simple rules. It is useful as a simple baseline to compare with other (real) classifiers. \n",
+    "\n",
+    "As previosly, we  import a function defined in the file [plotml.py](files/plotml.py) using the *magic command* **%run**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# display plots in the notebook \n",
+    "#%matplotlib inline\n",
+    "\n",
+    "# Run in a separate window to make it bigger\n",
+    "%matplotlib qt\n",
+    "%run plotml\n",
+    "plot_classifiers()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## References"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* [Classifier comparison¶](http://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html#example-classification-plot-classifier-comparison-py)\n",
+    "* [DummyClassifier ](http://scikit-learn.org/stable/modules/generated/sklearn.dummy.DummyClassifier.html)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Licence\n",
+    "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/).  \n",
+    "\n",
+    "© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid."
+   ]
+  }
+ ],
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+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
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+   "file_extension": ".py",
+   "mimetype": "text/x-python",
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+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
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+}