Delete xai/readme

XAI notebook

README.md

@@ -1,19 +1,21 @@
 # sitc
 Exercises for Intelligent Systems Course at Universidad Politécnica de Madrid, Telecommunication Engineering School. This material is used in the subjects
-- SITC (Sistemas Inteligentes y Tecnologías del Conocimiento) - Master Universitario de Ingeniería de Telecomunicación (MUIT)
+- CDAW (Ciencia de datos y aprendizaje en automático en la web de datos) - Master Universitario de Ingeniería de Telecomunicación (MUIT)
-- TIAD (Tecnologías Inteligentes de Análisis de Datos) - Master Universitario en Ingeniera de Redes y Servicios Telemáticos)
+- ABID (Analítica de Big Data) - Master Universitario en Ingeniera de Redes y Servicios Telemáticos)
 
 For following this course:
 - Follow the instructions to install the environment: https://github.com/gsi-upm/sitc/blob/master/python/1_1_Notebooks.ipynb (Just install 'conda')
-- Download the course: use 'https://github.com/gsi-upm/sitc'
+- Download the course: use 'https://github.com/gsi-upm/sitc' (or clone the repository to receive updates).
-- Run in a terminal in the foloder sitc: jupyter notebook (and enjoy)
+- Run in a terminal in the folder sitc: jupyter notebook (and enjoy)
 
 Topics
-* Python: quick introduction to Python
+* Python: a quick introduction to Python
 * ML-1: introduction to machine learning with scikit-learn
 * ML-2: introduction to machine learning with pandas and scikit-learn
+* ML-21: preprocessing and visualizatoin
 * ML-3: introduction to machine learning. Neural Computing
 * ML-4: introduction to Evolutionary Computing
 * ML-5: introduction to Reinforcement Learning
 * NLP: introduction to NLP
 * LOD: Linked Open Data, exercises and example code
+* SNA: Social Network Analysis

images/.p

@@ -0,0 +1 @@
+

lod/00_SPARQL_Tutorial.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](files/images/EscUpmPolit_p.gif \"UPM\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<header style=\"width:100%;position:relative\">\n",
+    "  <div style=\"width:80%;float:right;\">\n",
+    "    <h1>Course Notes for Learning Intelligent Systems</h1>\n",
+    "    <h3>Department of Telematic Engineering Systems</h3>\n",
+    "    <h5>Universidad Politécnica de Madrid. © Carlos A. Iglesias </h5>\n",
+    "  </div>\n",
+    "        <img style=\"width:15%;\" src=\"../logo.jpg\" alt=\"UPM\" />\n",
+    "</header>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "This lecture provides an introduction to RDF and the SPARQL query language.\n",
+    "\n",
+    "This is the first in a series of notebooks about SPARQL, which consists of:\n",
+    "\n",
+    "* This notebook, which explains basic concepts of RDF and SPARQL\n",
+    "* [A notebook](01_SPARQL_Introduction.ipynb) that provides an introduction to SPARQL through a collection of  exercises of increasing difficulty.\n",
+    "* [An optional notebook](02_SPARQL_Custom_Endpoint.ipynb) with queries to a custom dataset.\n",
+    "The dataset is meant to be done after the [RDF exercises](../rdf/RDF.ipynb) and it is out of the scope of this course.\n",
+    "You can consult it if you are interested."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# RDF basics\n",
+    "This section is taken from [[1](#1), [2](#2)].\n",
+    "\n",
+    "RDF allows us to make statements about resources. The format of these statements is simple. A statement always has the following structure:\n",
+    "\n",
+    "      <subject> <predicate> <object>\n",
+    "    \n",
+    "An RDF statement expresses a relationship between two resources. The **subject** and the **object** represent the two resources being related; the **predicate** represents the nature of their relationship.\n",
+    "The relationship is phrased in a directional way (from subject to object).\n",
+    "In RDF this relationship is known as a **property**.\n",
+    "Because RDF statements consist of three elements they are called **triples**.\n",
+    "\n",
+    "Here are some examples of RDF triples (informally expressed in pseudocode):\n",
+    "\n",
+    "      <Bob> <is a> <person>.\n",
+    "      <Bob> <is a friend of> <Alice>.\n",
+    "      \n",
+    "Resources are identified by [IRIs](https://en.wikipedia.org/wiki/Internationalized_Resource_Identifier), which can appear in all three positions of a triple. For example, the IRI for Leonardo da Vinci in DBpedia is:\n",
+    "\n",
+    "      <http://dbpedia.org/resource/Leonardo_da_Vinci>\n",
+    "\n",
+    "IRIs can be abbreviated as *prefixed names*. For example, \n",
+    "     PREFIX dbr: <http://dbpedia.org/resource/>\n",
+    "     <dbr:Leonardo_da_Vinci>\n",
+    "     \n",
+    "Objects can be literals: \n",
+    " * strings (e.g., \"plain string\" or \"string with language\"@en)\n",
+    " * numbers (e.g., \"13.4\"^^xsd:float)\n",
+    " * dates (e.g., )\n",
+    " * booleans\n",
+    " * etc.\n",
+    " \n",
+    "RDF data is stored in RDF repositories that expose SPARQL endpoints.\n",
+    "Let's query one of the most famous RDF repositories: [dbpedia](https://wiki.dbpedia.org/).\n",
+    "First, we should learn how to execute SPARQL in a notebook."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Executing SPARQL in a notebook\n",
+    "There are several ways to execute SPARQL in a notebook.\n",
+    "Some of the most popular are:\n",
+    "\n",
+    "* using libraries such as [sparql-client](https://pypi.org/project/sparql-client/) or [rdflib](https://rdflib.dev/sparqlwrapper/) that enable executing SPARQL within a Python3 kernel\n",
+    "* using other libraries. In our case, a light library has been developed (the file helpers.py) for accessing SPARQL endpoints using an HTTP connection.\n",
+    "* using the [graph notebook package](https://pypi.org/project/graph-notebook/)\n",
+    "* using a SPARQL kernel [sparql kernel](https://github.com/paulovn/sparql-kernel) instead of the Python3 kernel\n",
+    "\n",
+    "\n",
+    "We are going to use the second option to avoid installing new packages.\n",
+    "\n",
+    "To use the library, you need to:\n",
+    "\n",
+    "1. Import `sparql` from helpers (i.e., `helpers.py`, a file that is available in the github repository)\n",
+    "2. Use the `%%sparql` magic command to indicate the SPARQL endpoint and then the SPARQL code.\n",
+    "\n",
+    "Let's try it!\n",
+    "\n",
+    "# Queries agains DBPedia\n",
+    "\n",
+    "We are going to execute a SPARQL query against DBPedia. This section is based on [[8](#8)].\n",
+    "\n",
+    "First, we just create a query to retrieve arbitrary triples (subject, predicate, object) without any restriction (besides limiting the result to 10 triples)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from helpers import sparql"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%sparql https://dbpedia.org/sparql\n",
+    "\n",
+    "SELECT ?s ?p ?o\n",
+    "WHERE {\n",
+    "    ?s ?p ?o\n",
+    "}\n",
+    "LIMIT 10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Well, it worked, but the results are not particulary interesting. \n",
+    "Let's search for a famous football player, Fernando Torres.\n",
+    "\n",
+    "To do so, we will search for entities whose English \"human-readable representation\" (i.e., label) matches \"Fernando Torres\":"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%sparql https://dbpedia.org/sparql\n",
+    "\n",
+    "SELECT *\n",
+    "WHERE\n",
+    "     {\n",
+    "        ?athlete rdfs:label \"Fernando Torres\"@en \n",
+    "     }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Great, we found the IRI of the node: `http://dbpedia.org/resource/Fernando_Torres`\n",
+    "\n",
+    "Now we can start asking for more properties.\n",
+    "\n",
+    "To do so, go to http://dbpedia.org/resource/Fernando_Torres and you will see all the information available about Fernando Torres. Pay attention to the names of predicates to be able to create new queries. For example, we are interesting in knowing where Fernando Torres was born (`dbo:birthPlace`).\n",
+    "\n",
+    "Let's go!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%sparql https://dbpedia.org/sparql\n",
+    "\n",
+    "PREFIX dbo: <http://dbpedia.org/ontology/>\n",
+    "\n",
+    "SELECT *\n",
+    "WHERE\n",
+    "     {\n",
+    "        ?athlete rdfs:label \"Fernando Torres\"@en ;\n",
+    "                 dbo:birthPlace ?birthPlace .       \n",
+    "     }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we examine the SPARQL query, we find three blocks:\n",
+    "\n",
+    "* **PREFIX** section: IRIs of vocabularies and the prefix used below, to avoid long IRIs. e.g., by defining the `dbo` prefix in our example, the `dbo:birthPlace` below expands to `http://dbpedia.org/ontology/birthPlace`.\n",
+    "* **SELECT** section: variables we want to return (`*` is an abbreviation that selects all of the variables in a query)\n",
+    "* **WHERE** clause: triples where some elements are variables. These variables are bound during the query processing process and bounded variables are returned.\n",
+    "\n",
+    "Now take a closer look at the **WHERE** section.\n",
+    "We said earlier that triples are made out of three elements and each triple pattern should finish with a  period (`.`) (although the last pattern can omit this).\n",
+    "However, when two or more triple patterns share the same subject, we omit it all but the first one, and use ` ;` as separator.\n",
+    "If if both the subject and predicate are the same, we could use a coma `,` instead.\n",
+    "This allows us to avoid repetition and make queries more readable.\n",
+    "But don't forget the space before your separators (`;` and `.`).\n",
+    "\n",
+    "The result is interesting, we know he was born in Fuenlabrada, but we see an additional (wrong) value, the Spanish national football team. The conversion process from Wikipedia to DBPedia should still be tuned :).\n",
+    "\n",
+    "We can *fix* it, by adding some more constaints.\n",
+    "In our case, only want a birth place that is also a municipality (i.e., its type is `http://dbpedia.org/resource/Municipalities_of_Spain`).\n",
+    "Let's see!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%sparql https://dbpedia.org/sparql\n",
+    "\n",
+    "PREFIX dbo: <http://dbpedia.org/ontology/>\n",
+    "PREFIX dbr: <http://dbpedia.org/resource/>\n",
+    "\n",
+    "SELECT *\n",
+    "WHERE\n",
+    "     {\n",
+    "        ?athlete rdfs:label \"Fernando Torres\"@en ;\n",
+    "                 dbo:birthPlace ?birthPlace .\n",
+    "        ?birthPlace dbo:type dbr:Municipalities_of_Spain \n",
+    "     }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Great. Now it looks better.\n",
+    "Notice that we added a new prefix.\n",
+    "\n",
+    "Now, is Fuenlabrada is a big city?\n",
+    "Let's find out.\n",
+    "\n",
+    "**Hint**: you can find more subject / object / predicate nodes related to [Fuenlabrada])http://dbpedia.org/resource/Fuenlabrada) in the RDF graph just as we did before.\n",
+    "That is how we found the `dbo:areaTotal` property."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%sparql https://dbpedia.org/sparql\n",
+    "\n",
+    "PREFIX dbo: <http://dbpedia.org/ontology/>\n",
+    "PREFIX dbr: <http://dbpedia.org/resource/>\n",
+    "\n",
+    "SELECT *\n",
+    "WHERE\n",
+    "     {\n",
+    "        dbr:Fuenlabrada dbo:areaTotal ?area \n",
+    "     }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Well, it shows 39.1 km$^2$.\n",
+    "\n",
+    "Let's go back to our Fernando Torres.\n",
+    "What we are really insterested in is the name of the city he was born in, not its IRI.\n",
+    "As we saw before, the human-readable name is provided by the `rdfs:label` property:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%sparql https://dbpedia.org/sparql\n",
+    "\n",
+    "PREFIX dbo: <http://dbpedia.org/ontology/>\n",
+    "PREFIX dbp: <http://dbpedia.org/property/>\n",
+    "\n",
+    "SELECT *\n",
+    "WHERE\n",
+    "     {\n",
+    "        ?player rdfs:label \"Fernando Torres\"@en ;\n",
+    "                 dbo:birthPlace ?birthPlace .\n",
+    "        ?birthPlace dbo:type dbr:Municipalities_of_Spain ;\n",
+    "                    rdfs:label ?placeName        \n",
+    "                 \n",
+    "     }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Well, we are almost there. We see that we receive the city name in many languages. We want just the English name. Let's filter!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%sparql https://dbpedia.org/sparql\n",
+    "\n",
+    "PREFIX dbo: <http://dbpedia.org/ontology/>\n",
+    "PREFIX dbp: <http://dbpedia.org/property/>\n",
+    "\n",
+    "SELECT *\n",
+    "WHERE\n",
+    "     {\n",
+    "        ?player rdfs:label \"Fernando Torres\"@en ;\n",
+    "                 dbo:birthPlace ?birthPlace .\n",
+    "        ?birthPlace dbo:type dbr:Municipalities_of_Spain ;\n",
+    "                    rdfs:label ?placeName .\n",
+    "         FILTER ( LANG ( ?placeName ) = 'en' )\n",
+    "                 \n",
+    "     }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Awesome!\n",
+    "\n",
+    "But we said we don't care about the IRI of the place. We only want two pieces of data: Fernando's birth date and the name of his birthplace.\n",
+    "\n",
+    "Let's tune our query a bit more."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%sparql https://dbpedia.org/sparql\n",
+    "\n",
+    "PREFIX dbo: <http://dbpedia.org/ontology/>\n",
+    "PREFIX dbp: <http://dbpedia.org/property/>\n",
+    "\n",
+    "SELECT ?birthDate, ?placeName\n",
+    "WHERE\n",
+    "     {\n",
+    "        ?player rdfs:label \"Fernando Torres\"@en ;\n",
+    "                 dbo:birthDate ?birthDate ;\n",
+    "                 dbo:birthPlace ?birthPlace .\n",
+    "        ?birthPlace dbo:type dbr:Municipalities_of_Spain ;\n",
+    "                    rdfs:label ?placeName .\n",
+    "         FILTER ( LANG ( ?placeName ) = 'en' )\n",
+    "                 \n",
+    "     }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Great 😃\n",
+    "\n",
+    "Are there many football players born in Fuenlabrada? Let's find out!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%sparql https://dbpedia.org/sparql\n",
+    "\n",
+    "PREFIX dbo: <http://dbpedia.org/ontology/>\n",
+    "PREFIX dbp: <http://dbpedia.org/property/>\n",
+    "\n",
+    "SELECT *\n",
+    "WHERE\n",
+    "     {\n",
+    "        ?player a dbo:SoccerPlayer ;  \n",
+    "                  dbo:birthPlace dbr:Fuenlabrada .         \n",
+    "     }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Well, not that many. Observe we have used `a`.\n",
+    "It is just an abbreviation for `rdf:type`, both can be used interchangeably.\n",
+    "\n",
+    "If you want additional examples, you can follow the notebook by [Shawn Graham](https://github.com/o-date/sparql-and-lod/blob/master/sparql-intro.ipynb), which is based on the  SPARQL tutorial by Matthew Lincoln, available [here in English](https://programminghistorian.org/en/lessons/retired/graph-databases-and-SPARQL) and [here in Spanish](https://programminghistorian.org/es/lecciones/retirada/sparql-datos-abiertos-enlazados]). You have also a local copy of these tutorials together with this notebook [here in English](https://htmlpreview.github.io/?https://github.com/gsi-upm/sitc/blob/master/lod/tutorial/graph-databases-and-SPARQL.html) and [here in Spanish](https://htmlpreview.github.io/?https://github.com/gsi-upm/sitc/blob/master/lod/tutorial/sparql-datos-abiertos-enlazados.html). \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## References"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* <a id=\"1\">[1]</a> [SPARQL by Example. A Tutorial. Lee Feigenbaum. W3C, 2009](https://www.w3.org/2009/Talks/0615-qbe/#q1)\n",
+    "* <a id=\"2\">[2]</a> [RDF Primer W3C](https://www.w3.org/TR/rdf11-primer/)\n",
+    "* <a id=\"3\">[3]</a> [SPARQL queries of Beatles recording sessions](http://www.snee.com/bobdc.blog/2017/11/sparql-queries-of-beatles-reco.html)\n",
+    "* <a id=\"4\">[4]</a> [RDFLib documentation](https://rdflib.readthedocs.io/en/stable/).\n",
+    "* <a id=\"5\">[5]</a> [Wikidata Query Service query examples](https://www.wikidata.org/wiki/Wikidata:SPARQL_query_service/queries/examples)\n",
+    "* <a id=\"6\">[6]</a> [RDF Graph Data Model. Learn about the RDF graph model used by Stardog.](https://www.stardog.com/tutorials/data-model)\n",
+    "* <a id=\"7\">[7]</a> [Learn SPARQL Write Knowledge Graph queries using SPARQL with step-by-step examples.](https://www.stardog.com/tutorials/sparql/)\n",
+    "* <a id=\"8\">[8]</a> [Running Basic SPARQL Queries Against DBpedia.](https://medium.com/virtuoso-blog/dbpedia-basic-queries-bc1ac172cc09)\n",
+    "* <a id=\"8\">[9]</a> [Intro SPARQL based on painters.](https://github.com/o-date/sparql-and-lod/blob/master/sparql-intro.ipynb)."
+   ]
+  },
+  {
+   "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",
+    "©  Carlos A. Iglesias, Universidad Politécnica de Madrid."
+   ]
+  }
+ ],
+ "metadata": {
+  "datacleaner": {
+   "position": {
+    "top": "50px"
+   },
+   "python": {
+    "varRefreshCmd": "try:\n    print(_datacleaner.dataframe_metadata())\nexcept:\n    print([])"
+   },
+   "window_display": false
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.8.10"
+  },
+  "latex_envs": {
+   "LaTeX_envs_menu_present": true,
+   "autocomplete": true,
+   "bibliofile": "biblio.bib",
+   "cite_by": "apalike",
+   "current_citInitial": 1,
+   "eqLabelWithNumbers": true,
+   "eqNumInitial": 1,
+   "hotkeys": {
+    "equation": "Ctrl-E",
+    "itemize": "Ctrl-I"
+   },
+   "labels_anchors": false,
+   "latex_user_defs": false,
+   "report_style_numbering": false,
+   "user_envs_cfg": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

lod/01_SPARQL_Introduction.ipynb

@@ -6,11 +6,12 @@
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     "nbgrader": {
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      "grade": false,
      "grade_id": "cell-0bfe38f97f6ab2d2",
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-     "schema_version": 1,
+     "schema_version": 3,
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@@ -56,11 +57,12 @@
     "deletable": false,
     "editable": false,
     "nbgrader": {
-     "checksum": "99aecbad8f94966d92d72dc911d3ff99",
+     "cell_type": "markdown",
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+     "schema_version": 3,
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@@ -69,7 +71,7 @@
     "\n",
     "* This notebook\n",
     "* External SPARQL editors (optional)\n",
-    "    * YASGUI-GSI http://yasgui.cluster.gsi.dit.upm.es\n",
+    "    * YASGUI-GSI http://yasgui.gsi.upm.es\n",
     "    * DBpedia virtuoso http://dbpedia.org/sparql\n",
     "\n",
     "Using the YASGUI-GSI editor has several advantages over other options.\n",
@@ -93,18 +95,19 @@
     "deletable": false,
     "editable": false,
     "nbgrader": {
-     "checksum": "99e3107f9987cdddae7866dded27f165",
+     "cell_type": "markdown",
+     "checksum": "81894e9d65e5dd9f3b6e1c5f66804bf6",
      "grade": false,
      "grade_id": "cell-70ac24910356c3cf",
      "locked": true,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": false
     }
    },
    "source": [
     "## Instructions\n",
     "\n",
-    "We will be using a semantic server, available at: http://fuseki.cluster.gsi.dit.upm.es/sitc.\n",
+    "We will be using a semantic server, available at: http://fuseki.gsi.upm.es/sitc.\n",
     "\n",
     "This server contains a dataset about [Beatles songs](http://www.snee.com/bobdc.blog/2017/11/sparql-queries-of-beatles-reco.html), which we will query with SPARQL.\n",
     "\n",
@@ -122,11 +125,12 @@
     "deletable": false,
     "editable": false,
     "nbgrader": {
+     "cell_type": "markdown",
      "checksum": "1d332d3d11fd6b57f0ec0ac3c358c6cb",
      "grade": false,
      "grade_id": "cell-eb13908482825e42",
      "locked": true,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": false
     }
    },
@@ -144,11 +148,12 @@
     "deletable": false,
     "editable": false,
     "nbgrader": {
+     "cell_type": "code",
      "checksum": "aca7c5538b8fc53e99c92e94e6818c83",
      "grade": false,
      "grade_id": "cell-b3f3d92fa2100c3d",
      "locked": true,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": false
     }
    },
@@ -163,11 +168,12 @@
     "deletable": false,
     "editable": false,
     "nbgrader": {
+     "cell_type": "markdown",
      "checksum": "e896b6560e45d5c385a43aa85e3523c7",
      "grade": false,
      "grade_id": "cell-04410e75828c388d",
      "locked": true,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": false
     }
    },
@@ -193,11 +199,12 @@
     "deletable": false,
     "editable": false,
     "nbgrader": {
-     "checksum": "96ca90572d6b275fa515c6b976115257",
+     "cell_type": "markdown",
+     "checksum": "34710d3bb8e2cf826833a43adb7fb448",
      "grade": false,
      "grade_id": "cell-2a44c0da2c206d01",
      "locked": true,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": false
     }
    },
@@ -210,7 +217,7 @@
     "Some examples are:\n",
     "\n",
     "* DBpedia's virtuoso query editor https://dbpedia.org/sparql\n",
-    "* A javascript based client hosted at GSI: http://yasgui.cluster.gsi.dit.upm.es/\n",
+    "* A javascript based client hosted at GSI: http://yasgui.gsi.upm.es/\n",
     "\n",
     "[^1]: http://www.snee.com/bobdc.blog/2017/11/sparql-queries-of-beatles-reco.html"
    ]
@@ -221,11 +228,12 @@
     "deletable": false,
     "editable": false,
     "nbgrader": {
+     "cell_type": "markdown",
      "checksum": "79c60bd3d4c13f380aae5778c5ce7245",
      "grade": false,
      "grade_id": "cell-d645128d3af18117",
      "locked": true,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": false
     }
    },
@@ -241,11 +249,12 @@
     "deletable": false,
     "editable": false,
     "nbgrader": {
+     "cell_type": "markdown",
      "checksum": "f7428fe79cd33383dfd3b09a0d951b6e",
      "grade": false,
      "grade_id": "cell-8391a5322a9ad4a7",
      "locked": true,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": false
     }
    },
@@ -260,11 +269,12 @@
     "deletable": false,
     "editable": false,
     "nbgrader": {
+     "cell_type": "markdown",
      "checksum": "f6b5da583694dd5cc9326c670830875d",
      "grade": false,
      "grade_id": "cell-4f56a152e4d70c02",
      "locked": true,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": false
     }
    },
@@ -313,17 +323,18 @@
     "deletable": false,
     "editable": false,
     "nbgrader": {
-     "checksum": "7a9dc62ab639143c9fc13593e50500d4",
+     "cell_type": "code",
+     "checksum": "3bc71f851a33fa401d18ea3ab02cf61f",
      "grade": false,
      "grade_id": "cell-8ce8c954513f17e7",
      "locked": true,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": false
     }
    },
    "outputs": [],
    "source": [
-    "%%sparql http://fuseki.cluster.gsi.dit.upm.es/sitc/\n",
+    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
     "\n",
     "SELECT ?entity ?type\n",
     "WHERE {\n",
@@ -338,11 +349,12 @@
     "deletable": false,
     "editable": false,
     "nbgrader": {
+     "cell_type": "markdown",
      "checksum": "d6a79c2f5fd005a9e15a8f67dcfd4784",
      "grade": false,
      "grade_id": "cell-3d6d622c717c3950",
      "locked": true,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": false
     }
    },
@@ -375,17 +387,18 @@
    "metadata": {
     "deletable": false,
     "nbgrader": {
-     "checksum": "69e016b0224f410f03f6217ac30c03a8",
+     "cell_type": "code",
+     "checksum": "65be7168bedb4f6dc2f19e2138bab232",
      "grade": false,
      "grade_id": "cell-6e904d692b5facad",
      "locked": false,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": true
     }
    },
    "outputs": [],
    "source": [
-    "%%sparql http://fuseki.cluster.gsi.dit.upm.es/sitc/\n",
+    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
     "\n",
     "SELECT ?entity ?prop\n",
     "WHERE {\n",
@@ -401,12 +414,13 @@
     "deletable": false,
     "editable": false,
     "nbgrader": {
-     "checksum": "97bd5d5383bd94a72c7452bc33e4b0f9",
+     "cell_type": "code",
+     "checksum": "e78b57fa9baab578f5a4bd22dc499fca",
      "grade": true,
      "grade_id": "cell-3fc0d3c43dfd04a3",
      "locked": true,
-     "points": 0,
+     "points": 1,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": false
     }
    },
@@ -440,7 +454,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%sparql http://fuseki.cluster.gsi.dit.upm.es/sitc/\n",
+    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
     "\n",
     "SELECT ?type\n",
     "WHERE {\n",
@@ -465,7 +479,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%sparql http://fuseki.cluster.gsi.dit.upm.es/sitc/\n",
+    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
     "\n",
     "SELECT DISTINCT ?type\n",
     "WHERE {\n",
@@ -507,17 +521,18 @@
    "metadata": {
     "deletable": false,
     "nbgrader": {
-     "checksum": "47c4f68e342ffe59a3804de7b6a3909b",
+     "cell_type": "code",
+     "checksum": "35563ff455c7e8b1c91f61db97b2011b",
      "grade": false,
      "grade_id": "cell-e615f9a77c4bc9a5",
      "locked": false,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": true
     }
    },
    "outputs": [],
    "source": [
-    "%%sparql http://fuseki.cluster.gsi.dit.upm.es/sitc/\n",
+    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
     "\n",
     "SELECT DISTINCT ?property\n",
     "WHERE {\n",
@@ -532,12 +547,13 @@
     "deletable": false,
     "editable": false,
     "nbgrader": {
-     "checksum": "c9ffeba2d4ffc3e0b95f15a0ec6012c5",
+     "cell_type": "code",
+     "checksum": "7603c90d8c177e2e6678baa2f1b6af36",
      "grade": true,
      "grade_id": "cell-9168718938ab7347",
      "locked": true,
-     "points": 0,
+     "points": 1,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": false
     }
    },
@@ -569,7 +585,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%sparql http://fuseki.cluster.gsi.dit.upm.es/sitc/\n",
+    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
     "\n",
     "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
     "PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>\n",
@@ -638,17 +654,18 @@
    "metadata": {
     "deletable": false,
     "nbgrader": {
-     "checksum": "8b0faf938efc1a64a70515da3c132605",
+     "cell_type": "code",
+     "checksum": "069811507dbac4b86dc5d3adc82ba4ec",
      "grade": false,
      "grade_id": "cell-0223a51f609edcf9",
      "locked": false,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": true
     }
    },
    "outputs": [],
    "source": [
-    "%%sparql http://fuseki.cluster.gsi.dit.upm.es/sitc/\n",
+    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
     "\n",
     "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
     "PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>\n",
@@ -667,12 +684,13 @@
     "deletable": false,
     "editable": false,
     "nbgrader": {
-     "checksum": "e93d7336fd125d95996e60fd312a4e4d",
+     "cell_type": "code",
+     "checksum": "9833a3efa75c7e2784ef5d60aae2a13e",
      "grade": true,
      "grade_id": "cell-3c7943c6382c62f5",
      "locked": true,
-     "points": 0,
+     "points": 1,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": false
     }
    },
@@ -706,17 +724,18 @@
    "metadata": {
     "deletable": false,
     "nbgrader": {
-     "checksum": "271f2b194c2db4c558a46e8312b593e6",
+     "cell_type": "code",
+     "checksum": "b68a279085a1ed087f5e474a6602299e",
      "grade": false,
      "grade_id": "cell-8f43547dd788bb33",
      "locked": false,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": true
     }
    },
    "outputs": [],
    "source": [
-    "%%sparql http://fuseki.cluster.gsi.dit.upm.es/sitc/\n",
+    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
     "\n",
     "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
     "PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>\n",
@@ -735,12 +754,13 @@
     "deletable": false,
     "editable": false,
     "nbgrader": {
-     "checksum": "9f1f7cec8ce4674971543728ada86674",
+     "cell_type": "code",
+     "checksum": "b4461d243cc058b1828769cc906d4947",
      "grade": true,
      "grade_id": "cell-e13a1c921af2f6eb",
      "locked": true,
-     "points": 0,
+     "points": 1,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": false
     }
    },
@@ -770,11 +790,12 @@
     "\n",
     "SELECT *\n",
     "WHERE { ... }\n",
-    "ORDER BY <variable> <variable> ... DESC(<variable>) ASC(<variable>)\n",
+    "ORDER BY <variable> <variable> ... \n",
     "... other statements like LIMIT ...\n",
     "```\n",
     "\n",
-    "The results can be sorted in ascending or descending order, and using several variables."
+    "The results can be sorted in ascending or descending order, and using several variables.\n",
+    "By default the results are ordered in ascending order, but you can indicate the order using an optional modifier (`ASC(<variable>)`, or `DESC(<variable>)`). \n"
    ]
   },
   {
@@ -790,17 +811,18 @@
    "metadata": {
     "deletable": false,
     "nbgrader": {
-     "checksum": "9dcd9c6d51a61ac129cffa06e1463c66",
+     "cell_type": "code",
+     "checksum": "335403f01e484ce5563ff059e9764ff4",
      "grade": false,
      "grade_id": "cell-a0f0b9d9b05c9631",
      "locked": false,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": true
     }
    },
    "outputs": [],
    "source": [
-    "%%sparql http://fuseki.cluster.gsi.dit.upm.es/sitc/\n",
+    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
     "\n",
     "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
     "PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>\n",
@@ -820,12 +842,13 @@
     "deletable": false,
     "editable": false,
     "nbgrader": {
-     "checksum": "a044b3fd6b8bd4e098bbe4d818cb4e9f",
+     "cell_type": "code",
+     "checksum": "45530eb91cbc5b3fddcc93d96f07e579",
      "grade": true,
      "grade_id": "cell-bc012ca9d7ad2867",
      "locked": true,
-     "points": 0,
+     "points": 1,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": false
     }
    },
@@ -858,7 +881,7 @@
     "                    rdfs:label \"Ringo Starr\" .\n",
     "```\n",
     "\n",
-    "Using this structure, and the SPARQL statements you already know, to get the **names** of all musicians that collaborated in at least one song.\n"
+    "Using this structure, and the SPARQL statements you already know, get the **names** of all musicians that collaborated in at least one song.\n"
    ]
   },
   {
@@ -867,17 +890,18 @@
    "metadata": {
     "deletable": false,
     "nbgrader": {
-     "checksum": "9da7a62b6237078f5eab7e593a8eb590",
+     "cell_type": "code",
+     "checksum": "8fb253675d2e8510e2c6780b960721e5",
      "grade": false,
      "grade_id": "cell-523b963fa4e288d0",
      "locked": false,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": true
     }
    },
    "outputs": [],
    "source": [
-    "%%sparql http://fuseki.cluster.gsi.dit.upm.es/sitc/\n",
+    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
     "\n",
     "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
     "PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>\n",
@@ -898,12 +922,13 @@
     "deletable": false,
     "editable": false,
     "nbgrader": {
-     "checksum": "c8e3a929faf2afa72207c6921382654c",
+     "cell_type": "code",
+     "checksum": "f4474b302bc2f634b3b2ee6e1c7e7257",
      "grade": true,
      "grade_id": "cell-aa9a4e18d6fda225",
      "locked": true,
-     "points": 0,
+     "points": 1,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": false
     }
    },
@@ -930,13 +955,13 @@
     "\n",
     "Results can be aggregated using different functions.\n",
     "One of the simplest functions is `COUNT`.\n",
-    "The syntax for COUNT is:\n",
+    "The syntax for `COUNT` is:\n",
     "    \n",
     "```sparql\n",
     "SELECT (COUNT(?variable) as ?count_name)\n",
     "```\n",
     "\n",
-    "Use `COUNT` to get the number of songs in which Ringo collaborated."
+    "Use `COUNT` to get the number of songs in which Ringo collaborated. Your query should return a column named `number`."
    ]
   },
   {
@@ -945,17 +970,18 @@
    "metadata": {
     "deletable": false,
     "nbgrader": {
-     "checksum": "d8419711d2db43ad657e2658a1ea86c4",
+     "cell_type": "code",
+     "checksum": "c7b6620f5ba28b482197ab693cb7142a",
      "grade": false,
      "grade_id": "cell-e89d08031e30b299",
      "locked": false,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": true
     }
    },
    "outputs": [],
    "source": [
-    "%%sparql http://fuseki.cluster.gsi.dit.upm.es/sitc/\n",
+    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
     "\n",
     "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
     "PREFIX m: <http://learningsparql.com/ns/musician/>\n",
@@ -975,12 +1001,13 @@
     "deletable": false,
     "editable": false,
     "nbgrader": {
-     "checksum": "29404e07edf639cdc0ce0d82e654ec31",
+     "cell_type": "code",
+     "checksum": "c90e1427d7e48d9ae8abab40ff92e3b0",
      "grade": true,
      "grade_id": "cell-903d2be00885e1d2",
      "locked": true,
-     "points": 0,
+     "points": 1,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": false
     }
    },
@@ -1012,7 +1039,7 @@
     "\n",
     "Once results are grouped, they can be aggregated using any aggregation function, such as `COUNT`.\n",
     "\n",
-    "Using `GROUP BY` and `COUNT`, get the count of songs that use each instrument:"
+    "Using `GROUP BY` and `COUNT`, get the count of songs in which Ringo Starr has played each of the instruments:"
    ]
   },
   {
@@ -1021,17 +1048,18 @@
    "metadata": {
     "deletable": false,
     "nbgrader": {
-     "checksum": "7a0a7206384e7e1d9eb4450dd9e9871f",
+     "cell_type": "code",
+     "checksum": "7556bacb20c1fbd059dec165c982908d",
      "grade": false,
      "grade_id": "cell-1429e4eb5400dbc7",
      "locked": false,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": true
     }
    },
    "outputs": [],
    "source": [
-    "%%sparql http://fuseki.cluster.gsi.dit.upm.es/sitc/\n",
+    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
     "\n",
     "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
     "PREFIX m: <http://learningsparql.com/ns/musician/>\n",
@@ -1053,12 +1081,13 @@
     "deletable": false,
     "editable": false,
     "nbgrader": {
-     "checksum": "bd4dc379fea969d513be0ea97ee75922",
+     "cell_type": "code",
+     "checksum": "34a8432e8d4cea70994c8214ed0e5eb6",
      "grade": true,
      "grade_id": "cell-907aaf6001e27e50",
      "locked": true,
-     "points": 0,
+     "points": 1,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": false
     }
    },
@@ -1094,7 +1123,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%sparql http://fuseki.cluster.gsi.dit.upm.es/sitc/\n",
+    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
     "\n",
     "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
     "PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>\n",
@@ -1115,7 +1144,9 @@
     "Now, use the same principle to get the count of **different** instruments in each song.\n",
     "Some songs have several musicians playing the same instrument, but we only care about *different* instruments in each song.\n",
     "\n",
-    "Use `?number` for the count."
+    "Use `?song` for the song and `?number` for the count.\n",
+    "\n",
+    "Take into consideration that instruments are entities of type `i:Instrument`."
    ]
   },
   {
@@ -1124,17 +1155,18 @@
    "metadata": {
     "deletable": false,
     "nbgrader": {
-     "checksum": "4a231b4d6874dad435512b988c17c39e",
+     "cell_type": "code",
+     "checksum": "3139d9b7e620266946ffe1ae0cf67581",
      "grade": false,
      "grade_id": "cell-ee208c762d00da9c",
      "locked": false,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": true
     }
    },
    "outputs": [],
    "source": [
-    "%%sparql http://fuseki.cluster.gsi.dit.upm.es/sitc/\n",
+    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
     "\n",
     "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
     "PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>\n",
@@ -1144,6 +1176,8 @@
     "    [] a           s:Song ;\n",
     "       rdfs:label  ?song ;\n",
     "       ?instrument ?musician .\n",
+    "    \n",
+    "?instrument a s:Instrument .\n",
     "}\n",
     "# YOUR ANSWER HERE\n",
     "ORDER BY DESC(?number)"
@@ -1156,19 +1190,20 @@
     "deletable": false,
     "editable": false,
     "nbgrader": {
-     "checksum": "8118099bf14d9f0eb241c4d93ea6f0b9",
+     "cell_type": "code",
+     "checksum": "5abf6eb7a67ebc9f7612b876105c1960",
      "grade": true,
      "grade_id": "cell-ddeec32b8ac3d894",
      "locked": true,
-     "points": 0,
+     "points": 1,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": false
     }
    },
    "outputs": [],
    "source": [
     "s = solution()\n",
-    "assert s['columns']['number'][0] == '27'"
+    "assert s['columns']['number'][0] == '25'"
    ]
   },
   {
@@ -1193,7 +1228,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%sparql http://fuseki.cluster.gsi.dit.upm.es/sitc/\n",
+    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
     "\n",
     "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
     "PREFIX i: <http://learningsparql.com/ns/instrument/>\n",
@@ -1213,10 +1248,10 @@
    "metadata": {},
    "source": [
     "However, there are some songs that do not have a vocalist (at least, in the dataset).\n",
-    "Those songs will not appear in the list above, because we they do not match part of the `WHERE` clause.\n",
+    "Those songs will not appear in the list above, because they do not match part of the `WHERE` clause.\n",
     "\n",
     "In these cases, we can specify optional values in a query using the `OPTIONAL` keyword.\n",
-    "When a set of clauses are inside an OPTIONAL group, the SPARQL endpoint will try to use them in the query.\n",
+    "When a set of clauses are inside an `OPTIONAL` group, the SPARQL endpoint will try to use them in the query.\n",
     "If there are no results for that part of the query, the variables it specifies will not be bound (i.e. they will be empty).\n",
     "\n",
     "To exemplify this, we can use a property that **does not exist in the dataset**:"
@@ -1228,7 +1263,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%sparql http://fuseki.cluster.gsi.dit.upm.es/sitc/\n",
+    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
     "\n",
     "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
     "PREFIX i: <http://learningsparql.com/ns/instrument/>\n",
@@ -1261,17 +1296,18 @@
    "metadata": {
     "deletable": false,
     "nbgrader": {
-     "checksum": "4b0a0854457c37640aad67f375ed3a17",
+     "cell_type": "code",
+     "checksum": "3bc508872193750d57d07efbf334c212",
      "grade": false,
      "grade_id": "cell-dcd68c45c1608a28",
      "locked": false,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": true
     }
    },
    "outputs": [],
    "source": [
-    "%%sparql http://fuseki.cluster.gsi.dit.upm.es/sitc/\n",
+    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
     "\n",
     "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
     "PREFIX i: <http://learningsparql.com/ns/instrument/>\n",
@@ -1294,12 +1330,13 @@
     "deletable": false,
     "editable": false,
     "nbgrader": {
-     "checksum": "f7122b2284b5d59d59ce4a2925f0bb21",
+     "cell_type": "code",
+     "checksum": "69edef3121b8dfab385a00cd181c956f",
      "grade": true,
      "grade_id": "cell-1e706b9c1c1331bc",
      "locked": true,
-     "points": 0,
+     "points": 1,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": false
     }
    },
@@ -1343,17 +1380,18 @@
    "metadata": {
     "deletable": false,
     "nbgrader": {
-     "checksum": "09621e7af911faf39a834e8281bc6d1f",
+     "cell_type": "code",
+     "checksum": "300df0a3cf9729dd4814b3153b2fedb4",
      "grade": false,
      "grade_id": "cell-0c7cc924a13d792a",
      "locked": false,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": true
     }
    },
    "outputs": [],
    "source": [
-    "%%sparql http://fuseki.cluster.gsi.dit.upm.es/sitc/\n",
+    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
     "\n",
     "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
     "PREFIX i: <http://learningsparql.com/ns/instrument/>\n",
@@ -1379,12 +1417,13 @@
     "deletable": false,
     "editable": false,
     "nbgrader": {
-     "checksum": "cebff8ce42f3f36923e81e083a23d24c",
+     "cell_type": "code",
+     "checksum": "22d6fcdb72a8b2c5ab496cdbb5e2740a",
      "grade": true,
      "grade_id": "cell-2541abc93ab4d506",
      "locked": true,
-     "points": 0,
+     "points": 1,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": false
     }
    },
@@ -1416,17 +1455,18 @@
    "metadata": {
     "deletable": false,
     "nbgrader": {
-     "checksum": "ea9797f3b2d001ea41d7fa7a5170d5fb",
+     "cell_type": "code",
+     "checksum": "e4e898c8a16b8aa5865dfde2f6e68ec6",
      "grade": false,
      "grade_id": "cell-d750b6d64c6aa0a7",
      "locked": false,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": true
     }
    },
    "outputs": [],
    "source": [
-    "%%sparql http://fuseki.cluster.gsi.dit.upm.es/sitc/\n",
+    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
     "\n",
     "PREFIX  rdfs:  <http://www.w3.org/2000/01/rdf-schema#> \n",
     "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
@@ -1469,7 +1509,9 @@
    "source": [
     "Now, count how many instruments each musician have played in a song.\n",
     "\n",
-    "**Do not count lead (`i:vocals`) or backing vocals (`i:backingvocals`) as instruments**."
+    "**Do not count lead (`i:vocals`) or backing vocals (`i:backingvocals`) as instruments**.\n",
+    "\n",
+    "Use `?musician` for the musician and `?number` for the count."
    ]
   },
   {
@@ -1478,17 +1520,18 @@
    "metadata": {
     "deletable": false,
     "nbgrader": {
-     "checksum": "2d82df272d43f678d3b19bf0b41530c1",
+     "cell_type": "code",
+     "checksum": "fade6ab714376e0eabfa595dd6bd6a8b",
      "grade": false,
      "grade_id": "cell-2f5aa516f8191787",
      "locked": false,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": true
     }
    },
    "outputs": [],
    "source": [
-    "%%sparql http://fuseki.cluster.gsi.dit.upm.es/sitc/\n",
+    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
     "\n",
     "PREFIX  rdfs:  <http://www.w3.org/2000/01/rdf-schema#> \n",
     "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
@@ -1513,12 +1556,13 @@
     "deletable": false,
     "editable": false,
     "nbgrader": {
-     "checksum": "bc83dd9577c9111b1f0ef5bd40c4ec08",
+     "cell_type": "code",
+     "checksum": "33e93ec2a3d1f9eb4b0310d4651b74c2",
      "grade": true,
      "grade_id": "cell-bcd0f7e26b6c11c2",
      "locked": true,
-     "points": 0,
+     "points": 1,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": false
     }
    },
@@ -1533,7 +1577,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Which songs had Ringo in dums OR Lennon in lead vocals? (UNION)"
+    "### Which songs had Ringo in drums OR Lennon in lead vocals? (UNION)"
    ]
   },
   {
@@ -1567,17 +1611,18 @@
    "metadata": {
     "deletable": false,
     "nbgrader": {
-     "checksum": "a1e20e2be817a592683dea89eed0120e",
+     "cell_type": "code",
+     "checksum": "09262d81449c498c37e4b9d9b1dcdfed",
      "grade": false,
      "grade_id": "cell-d3a742bd87d9c793",
      "locked": false,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": true
     }
    },
    "outputs": [],
    "source": [
-    "%%sparql http://fuseki.cluster.gsi.dit.upm.es/sitc/\n",
+    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
     "\n",
     "PREFIX  rdfs:  <http://www.w3.org/2000/01/rdf-schema#> \n",
     "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
@@ -1597,18 +1642,19 @@
     "deletable": false,
     "editable": false,
     "nbgrader": {
-     "checksum": "087630476d73bb415b065fafbd6024f0",
+     "cell_type": "code",
+     "checksum": "11061e79ec06ccb3a9c496319a528366",
      "grade": true,
      "grade_id": "cell-409402df0e801d09",
      "locked": true,
-     "points": 0,
+     "points": 1,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": false
     }
    },
    "outputs": [],
    "source": [
-    "assert len(solution()['tuples']) == 246"
+    "assert len(solution()['tuples']) == 209"
    ]
   },
   {
@@ -1648,17 +1694,18 @@
    "metadata": {
     "deletable": false,
     "nbgrader": {
-     "checksum": "1d2cb88412c89c35861a4f9fccea3bf2",
+     "cell_type": "code",
+     "checksum": "9ddd2d1f50f841b889bfd29b175d06da",
      "grade": false,
      "grade_id": "cell-9d1ec854eb530235",
      "locked": false,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": true
     }
    },
    "outputs": [],
    "source": [
-    "%%sparql http://fuseki.cluster.gsi.dit.upm.es/sitc/\n",
+    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
     "\n",
     "PREFIX  rdfs:  <http://www.w3.org/2000/01/rdf-schema#> \n",
     "\n",
@@ -1680,12 +1727,13 @@
     "deletable": false,
     "editable": false,
     "nbgrader": {
-     "checksum": "aa20aa4d11632ea5bd6004df3187d979",
+     "cell_type": "code",
+     "checksum": "0ea5496acd1c3edd9e188b351690a533",
      "grade": true,
      "grade_id": "cell-a79c688b4566dbe8",
      "locked": true,
-     "points": 0,
+     "points": 1,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": false
     }
    },
@@ -1729,7 +1777,9 @@
     "\n",
     "Using `GROUP_CONCAT`, get a list of the instruments that each musician could play.\n",
     "\n",
-    "You can consult how to use GROUP_CONCAT [here](https://www.w3.org/TR/sparql11-query/)."
+    "You can consult how to use GROUP_CONCAT [here](https://www.w3.org/TR/sparql11-query/).\n",
+    "\n",
+    "Use `?musician` for the musician and `?instruments` for the list of instruments."
    ]
   },
   {
@@ -1738,17 +1788,18 @@
    "metadata": {
     "deletable": false,
     "nbgrader": {
-     "checksum": "508b7f8656e849838aa93cd38f1c6635",
+     "cell_type": "code",
+     "checksum": "d18e8b6e1d32aed395a533febb29fcb5",
      "grade": false,
      "grade_id": "cell-7ea1f5154cdd8324",
      "locked": false,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": true
     }
    },
    "outputs": [],
    "source": [
-    "%%sparql http://fuseki.cluster.gsi.dit.upm.es/sitc/\n",
+    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
     "PREFIX  rdfs:  <http://www.w3.org/2000/01/rdf-schema#> \n",
     "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
     "PREFIX i: <http://learningsparql.com/ns/instrument/>\n",
@@ -1773,7 +1824,9 @@
     "\n",
     "You can check if a string or URI matches a regular expression with `regex(?variable, \"<regex>\", \"i\")`.\n",
     "\n",
-    "The documentation for regular expressions in SPARQL is [here](https://www.w3.org/TR/rdf-sparql-query/)."
+    "The documentation for regular expressions in SPARQL is [here](https://www.w3.org/TR/rdf-sparql-query/).\n",
+    "\n",
+    "Use `?instrument` for the instrument and `?ins` for the url of the type."
    ]
   },
   {
@@ -1782,17 +1835,18 @@
    "metadata": {
     "deletable": false,
     "nbgrader": {
-     "checksum": "cff1f9c034393f8af055e1f930d5fe32",
+     "cell_type": "code",
+     "checksum": "f926fa3a3568d122454a12312859cda1",
      "grade": false,
      "grade_id": "cell-b6bee887a1b1fc60",
      "locked": false,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": true
     }
    },
    "outputs": [],
    "source": [
-    "%%sparql http://fuseki.cluster.gsi.dit.upm.es/sitc/\n",
+    "%%sparql http://fuseki.gsi.upm.es/sitc/\n",
     "PREFIX  rdfs:  <http://www.w3.org/2000/01/rdf-schema#> \n",
     "PREFIX s: <http://learningsparql.com/ns/schema/>\n",
     "PREFIX i: <http://learningsparql.com/ns/instrument/>\n",
@@ -1830,7 +1884,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1844,9 +1898,22 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.8.10"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

lod/02_SPARQL_Custom_Endpoint.ipynb

@@ -6,11 +6,12 @@
     "deletable": false,
     "editable": false,
     "nbgrader": {
+     "cell_type": "markdown",
      "checksum": "7276f055a8c504d3c80098c62ed41a4f",
      "grade": false,
      "grade_id": "cell-0bfe38f97f6ab2d2",
      "locked": true,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": false
     }
    },
@@ -31,11 +32,12 @@
     "deletable": false,
     "editable": false,
     "nbgrader": {
+     "cell_type": "markdown",
      "checksum": "42642609861283bc33914d16750b7efa",
      "grade": false,
      "grade_id": "cell-0cd673883ee592d1",
      "locked": true,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": false
     }
    },
@@ -59,11 +61,12 @@
     "deletable": false,
     "editable": false,
     "nbgrader": {
+     "cell_type": "markdown",
      "checksum": "a3ecb4b300a5ab82376a4a8cb01f7e6b",
      "grade": false,
      "grade_id": "cell-10264483046abcc4",
      "locked": true,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": false
     }
    },
@@ -80,11 +83,12 @@
     "deletable": false,
     "editable": false,
     "nbgrader": {
+     "cell_type": "markdown",
      "checksum": "2fedf0d73fc90104d1ab72c3413dfc83",
      "grade": false,
      "grade_id": "cell-4f8492996e74bf20",
      "locked": true,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": false
     }
    },
@@ -100,11 +104,12 @@
     "deletable": false,
     "editable": false,
     "nbgrader": {
+     "cell_type": "markdown",
      "checksum": "c5f8646518bd832a47d71f9d3218237a",
      "grade": false,
      "grade_id": "cell-eb13908482825e42",
      "locked": true,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": false
     }
    },
@@ -148,7 +153,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%sparql http://fuseki.cluster.gsi.dit.upm.es/hotels\n",
+    "%%sparql http://fuseki.gsi.upm.es/hotels\n",
     "    \n",
     "SELECT ?g (COUNT(?s) as ?count) WHERE {\n",
     "    GRAPH ?g {\n",
@@ -160,14 +165,12 @@
    ]
   },
   {
-   "cell_type": "code",
+   "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "You should see many graphs, with different triple counts.\n",
     "\n",
-    "The biggest one should be http://fuseki.cluster.gsi.dit.upm.es/synthetic"
+    "The biggest one should be http://fuseki.gsi.upm.es/synthetic"
    ]
   },
   {
@@ -183,11 +186,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%sparql http://fuseki.cluster.gsi.dit.upm.es/hotels\n",
+    "%%sparql http://fuseki.gsi.upm.es/hotels\n",
     "    \n",
     "SELECT *\n",
     "WHERE {\n",
-    "    GRAPH <http://fuseki.cluster.gsi.dit.upm.es/synthetic>{\n",
+    "    GRAPH <http://fuseki.gsi.upm.es/synthetic>{\n",
     "    ?s ?p ?o .\n",
     "    }\n",
     "}\n",
@@ -233,13 +236,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%sparql http://fuseki.cluster.gsi.dit.upm.es/hotels\n",
+    "%%sparql http://fuseki.gsi.upm.es/hotels\n",
     "\n",
     "PREFIX schema: <http://schema.org/>\n",
     "    \n",
     "SELECT ?s ?o\n",
     "WHERE {\n",
-    "    GRAPH <http://fuseki.cluster.gsi.dit.upm.es/35c20a49f8c6581be1cf7bd56d12d131>{\n",
+    "    GRAPH <http://fuseki.gsi.upm.es/35c20a49f8c6581be1cf7bd56d12d131>{\n",
     "        ?s a ?o .\n",
     "    }\n",
     "\n",
@@ -264,11 +267,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%sparql http://fuseki.cluster.gsi.dit.upm.es/hotels\n",
+    "%%sparql http://fuseki.gsi.upm.es/hotels\n",
     "    \n",
     "SELECT *\n",
     "WHERE {\n",
-    "    GRAPH <http://fuseki.cluster.gsi.dit.upm.es/synthetic>{\n",
+    "    GRAPH <http://fuseki.gsi.upm.es/synthetic>{\n",
     "    ?s ?p ?o .\n",
     "    }\n",
     "}\n",
@@ -295,7 +298,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%sparql http://fuseki.cluster.gsi.dit.upm.es/hotels\n",
+    "%%sparql http://fuseki.gsi.upm.es/hotels\n",
     "\n",
     "PREFIX schema: <http://schema.org/>\n",
     "    \n",
@@ -308,7 +311,7 @@
     "        SELECT ?g\n",
     "        WHERE {\n",
     "           GRAPH ?g {}\n",
-    "           FILTER (str(?g) != 'http://fuseki.cluster.gsi.dit.upm.es/synthetic')\n",
+    "           FILTER (str(?g) != 'http://fuseki.gsi.upm.es/synthetic')\n",
     "        }\n",
     "    }\n",
     "\n",
@@ -339,12 +342,13 @@
    "metadata": {
     "deletable": false,
     "nbgrader": {
+     "cell_type": "code",
      "checksum": "860c3977cd06736f1342d535944dbb63",
      "grade": true,
      "grade_id": "cell-9bd08e4f5842cb89",
      "locked": false,
      "points": 0,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": true
     }
    },
@@ -366,12 +370,13 @@
    "metadata": {
     "deletable": false,
     "nbgrader": {
+     "cell_type": "code",
      "checksum": "1946a7ed4aba8d168bb3fad898c05651",
      "grade": true,
      "grade_id": "cell-9dc1c9033198bb18",
      "locked": false,
      "points": 0,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": true
     }
    },
@@ -393,12 +398,13 @@
    "metadata": {
     "deletable": false,
     "nbgrader": {
+     "cell_type": "code",
      "checksum": "6714abc5226618b76dc4c1aaed6d1a49",
      "grade": true,
      "grade_id": "cell-6c18003ced54be23",
      "locked": false,
      "points": 0,
-     "schema_version": 1,
+     "schema_version": 3,
      "solution": true
     }
    },
@@ -435,7 +441,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -449,7 +455,20 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.8.10"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
   }
  },
  "nbformat": 4,

lod/04_SPARQL_Advanced.ipynb

@@ -0,0 +1,652 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "7276f055a8c504d3c80098c62ed41a4f",
+     "grade": false,
+     "grade_id": "cell-0bfe38f97f6ab2d2",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "<header style=\"width:100%;position:relative\">\n",
+    "  <div style=\"width:80%;float:right;\">\n",
+    "    <h1>Course Notes for Learning Intelligent Systems</h1>\n",
+    "    <h3>Department of Telematic Engineering Systems</h3>\n",
+    "    <h5>Universidad Politécnica de Madrid</h5>\n",
+    "  </div>\n",
+    "        <img style=\"width:15%;\" src=\"../logo.jpg\" alt=\"UPM\" />\n",
+    "</header>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "bd478e6253226d24ba7f33cb9f6ba706",
+     "grade": false,
+     "grade_id": "cell-0cd673883ee592d1",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "## Advanced SPARQL\n",
+    "\n",
+    "This notebook complements [the SPARQL notebook](./01_SPARQL.ipynb) with some advanced commands.\n",
+    "\n",
+    "If you have not completed the exercises in the previous notebook, please do so before continuing.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "9ea4fd529653214745b937d5fc4559e5",
+     "grade": false,
+     "grade_id": "cell-10264483046abcc4",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "## Objectives\n",
+    "\n",
+    "* To cover some SPARQL concepts that are less frequently used "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "2fedf0d73fc90104d1ab72c3413dfc83",
+     "grade": false,
+     "grade_id": "cell-4f8492996e74bf20",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "## Tools\n",
+    "\n",
+    "See [the SPARQL notebook](./01_SPARQL_Introduction.ipynb#Tools)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "c5f8646518bd832a47d71f9d3218237a",
+     "grade": false,
+     "grade_id": "cell-eb13908482825e42",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "Run this line to enable the `%%sparql` magic command."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from helpers import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Working with dates"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To explore dates, we will focus on our Writers example.\n",
+    "\n",
+    "First, search for writers born in the XX century.\n",
+    "You can use a special filter, knowing that `\"2000\"^^xsd:date` is the first date of year 2000."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "1a23c8b9a53f7ae28f28b1c23b9706b5",
+     "grade": false,
+     "grade_id": "cell-ab7755944d46f9ca",
+     "locked": false,
+     "schema_version": 3,
+     "solution": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%%sparql https://dbpedia.org/sparql\n",
+    "\n",
+    "PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>\n",
+    "PREFIX dct: <http://purl.org/dc/terms/>\n",
+    "PREFIX dbc: <http://dbpedia.org/resource/Category:>\n",
+    "PREFIX dbo: <http://dbpedia.org/ontology/>\n",
+    "PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>\n",
+    "\n",
+    "SELECT ?escritor ?nombre (year(?fechaNac) as ?nac)\n",
+    "WHERE {\n",
+    "    ?escritor dct:subject dbc:Spanish_novelists ;\n",
+    "              rdfs:label ?nombre ;\n",
+    "              dbo:birthDate ?fechaNac .\n",
+    "    FILTER(lang(?nombre) = \"es\") .\n",
+    "    # YOUR ANSWER HERE\n",
+    "}\n",
+    "# YOUR ANSWER HERE\n",
+    "LIMIT 1000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "e261d808f509c1e29227db94d1eab784",
+     "grade": true,
+     "grade_id": "cell-cf3821f2d33fb0f6",
+     "locked": true,
+     "points": 0,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "assert 'Ramiro Ledesma' in solution()['columns']['nombre']\n",
+    "assert 'Ray Loriga' in solution()['columns']['nombre']\n",
+    "assert all(int(x) > 1899 and int(x) < 2001 for x in solution()['columns']['nac'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, get the list of Spanish novelists that are still alive.\n",
+    "\n",
+    "A person is alive if their death date is not defined and the were born less than 100 years ago.\n",
+    "\n",
+    "Remember, we can check whether the optional value for a key was bound in a SPARQL query using `BOUND(?key)`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "e4579d551790c33ba4662562c6a05d99",
+     "grade": false,
+     "grade_id": "cell-474b1a72dec6827c",
+     "locked": false,
+     "schema_version": 3,
+     "solution": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%%sparql\n",
+    "\n",
+    "PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>\n",
+    "PREFIX dct:<http://purl.org/dc/terms/>\n",
+    "PREFIX dbc:<http://dbpedia.org/resource/Category:>\n",
+    "PREFIX dbo:<http://dbpedia.org/ontology/>\n",
+    "\n",
+    "SELECT ?escritor, ?nombre, year(?fechaNac) as ?nac\n",
+    "\n",
+    "WHERE {\n",
+    "    ?escritor dct:subject dbc:Spanish_novelists .\n",
+    "    ?escritor rdfs:label ?nombre .\n",
+    "    ?escritor dbo:birthDate ?fechaNac .\n",
+    "# YOUR ANSWER HERE\n",
+    "    FILTER(lang(?nombre) = \"es\") .\n",
+    "}\n",
+    "# YOUR ANSWER HERE\n",
+    "LIMIT 1000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "770bbddef5210c28486a1929e4513ada",
+     "grade": true,
+     "grade_id": "cell-46b62dd2856bc919",
+     "locked": true,
+     "points": 0,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "assert 'Fernando Arrabal' in solution()['columns']['nombre']\n",
+    "assert 'Albert Espinosa' in solution()['columns']['nombre']\n",
+    "for year in solution()['columns']['nac']:\n",
+    "    assert int(year) >= 1918"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Working with badly formatted dates (OPTIONAL!)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, get the list of Spanish novelists that died before their fifties (i.e. younger than 50 years old), or that aren't 50 years old yet.\n",
+    "\n",
+    "For the sake of simplicity, you can use the `year(<date>)` function.\n",
+    "\n",
+    "Hint: you can use boolean logic in your filters (e.g. `&&` and `||`).\n",
+    "\n",
+    "Hint 2: Some dates are not formatted properly, which makes some queries fail when they shouldn't. As a workaround, you could convert the date to string, and back to date again: `xsd:dateTime(str(?date))`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "e55173801ab36337ad356a1bc286dbd1",
+     "grade": false,
+     "grade_id": "cell-ceefd3c8fbd39d79",
+     "locked": false,
+     "schema_version": 3,
+     "solution": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%%sparql https://dbpedia.org/sparql\n",
+    "\n",
+    "PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>\n",
+    "PREFIX dct:<http://purl.org/dc/terms/>\n",
+    "PREFIX dbc:<http://dbpedia.org/resource/Category:>\n",
+    "PREFIX dbo:<http://dbpedia.org/ontology/>\n",
+    "\n",
+    "SELECT ?escritor, ?nombre, year(?fechaNac) as ?nac, ?fechaDef\n",
+    "\n",
+    "WHERE {\n",
+    "    ?escritor dct:subject dbc:Spanish_novelists .\n",
+    "    ?escritor rdfs:label ?nombre .\n",
+    "    ?escritor dbo:birthDate ?fechaNac .\n",
+    "    # YOUR ANSWER HERE\n",
+    "}\n",
+    "# YOUR ANSWER HERE\n",
+    "LIMIT 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "1b77cfaefb8b2ec286ce7b0c70804fe0",
+     "grade": true,
+     "grade_id": "cell-461cd6ccc6c2dc79",
+     "locked": true,
+     "points": 0,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "assert 'Javier Sierra' in solution()['columns']['nombre']\n",
+    "assert 'http://dbpedia.org/resource/José_Ángel_Mañas' in solution()['columns']['escritor']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Regular expressions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Regular expressions](https://www.w3.org/TR/rdf-sparql-query/#funcex-regex) are a very powerful tool, but we will only cover the basics in this exercise.\n",
+    "\n",
+    "In essence, regular expressions match strings against patterns.\n",
+    "In their simplest form, they can be used to find substrings within a variable.\n",
+    "For instance, using `regex(?label, \"substring\")` would only match if and only if the `?label` variable contains `substring`.\n",
+    "But regular expressions can be more complex than that.\n",
+    "For instance, we can find patterns such as: a 10 digit number, a 5 character long string, or variables without whitespaces.\n",
+    "\n",
+    "The syntax of the regex function is the following:\n",
+    "\n",
+    "```\n",
+    "regex(?variable, \"pattern\", \"flags\")\n",
+    "```\n",
+    "\n",
+    "Flags are optional configuration options for the regular expression, such as *do not care about case* (`i` flag).\n",
+    "\n",
+    "As an example, let us find the cities in Madrid that contain \"de\" in their name."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%sparql https://dbpedia.org/sparql\n",
+    "\n",
+    "SELECT ?localidad\n",
+    "WHERE {\n",
+    "    ?localidad <http://dbpedia.org/ontology/isPartOf> <http://dbpedia.org/resource/Community_of_Madrid> .\n",
+    "    ?localidad rdfs:label ?nombre .\n",
+    "    FILTER (lang(?nombre) = \"es\" ).\n",
+    "    FILTER regex(?nombre, \"de\", \"i\")\n",
+    "}\n",
+    "LIMIT 10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, use regular expressions to find Spanish novelists whose **first name** is Juan.\n",
+    "In other words, their name **starts with** \"Juan\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "b70a9a4f102c253e864d2e8aec79ce81",
+     "grade": false,
+     "grade_id": "cell-a57d3546a812f689",
+     "locked": false,
+     "schema_version": 3,
+     "solution": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%%sparql https://dbpedia.org/sparql\n",
+    "\n",
+    "PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>\n",
+    "PREFIX dct:<http://purl.org/dc/terms/>\n",
+    "PREFIX dbc:<http://dbpedia.org/resource/Category:>\n",
+    "PREFIX dbr:<http://dbpedia.org/resource/>\n",
+    "PREFIX dbo:<http://dbpedia.org/ontology/>\n",
+    "\n",
+    "# YOUR ANSWER HERE\n",
+    "\n",
+    "WHERE {\n",
+    "    {\n",
+    "        ?escritor dct:subject dbc:Spanish_poets .\n",
+    "    }\n",
+    "    UNION {\n",
+    "        ?escritor dct:subject dbc:Spanish_novelists .\n",
+    "    }\n",
+    "    ?escritor rdfs:label ?nombre\n",
+    "    FILTER(lang(?nombre) = \"es\") .\n",
+    "# YOUR ANSWER HERE\n",
+    "}\n",
+    "ORDER BY ?nombre\n",
+    "LIMIT 1000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "66db9abddfafa91c2dc25577457f71fb",
+     "grade": true,
+     "grade_id": "cell-c149fe65008f39a9",
+     "locked": true,
+     "points": 0,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "assert len(solution()['columns']['nombre']) > 15\n",
+    "for i in solution()['columns']['nombre']:\n",
+    "    assert 'Juan' in i\n",
+    "assert \"Robert Juan-Cantavella\" not in solution()['columns']['nombre']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "1be6d6e4d8e74240ef07deffcbe5e71a",
+     "grade": false,
+     "grade_id": "cell-0c2f0113d97dc9de",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "## Group concat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "c8dbb73a781bd24080804f289a1cea0b",
+     "grade": false,
+     "grade_id": "asdasdasdddddddddddasdasdsad",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "Sometimes, it is useful to aggregate results from form different rows.\n",
+    "For instance, we might want to get a comma-separated list of the names in each each autonomous community in Spain.\n",
+    "\n",
+    "In those cases, we can use the `GROUP_CONCAT` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%sparql https://dbpedia.org/sparql\n",
+    "\n",
+    "PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>\n",
+    "PREFIX dbo: <http://dbpedia.org/ontology/>\n",
+    "PREFIX dbr: <http://dbpedia.org/resource/>\n",
+    "        \n",
+    "SELECT ?com, GROUP_CONCAT(?name, \",\") as ?places  # notice how we rename the variable\n",
+    "\n",
+    "WHERE {\n",
+    "    ?com dct:subject dbc:Autonomous_communities_of_Spain .\n",
+    "    ?localidad dbo:subdivision ?com ;\n",
+    "             rdfs:label ?name .\n",
+    "    FILTER (lang(?name)=\"es\")\n",
+    "}\n",
+    "\n",
+    "ORDER BY ?com\n",
+    "LIMIT 100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "markdown",
+     "checksum": "4779fb61645634308d0ed01e0c88e8a4",
+     "grade": false,
+     "grade_id": "asdiopjasdoijasdoijasd",
+     "locked": true,
+     "schema_version": 3,
+     "solution": false
+    }
+   },
+   "source": [
+    "Try it yourself, to get a list of works by each of the authors in this query:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "e5d87d1d8eba51c510241ba75981a597",
+     "grade": false,
+     "grade_id": "cell-2e3de17c75047652",
+     "locked": false,
+     "schema_version": 3,
+     "solution": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%%sparql https://dbpedia.org/sparql\n",
+    "\n",
+    "PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>\n",
+    "PREFIX dct:<http://purl.org/dc/terms/>\n",
+    "PREFIX dbc:<http://dbpedia.org/resource/Category:>\n",
+    "PREFIX dbr:<http://dbpedia.org/resource/>\n",
+    "PREFIX dbo:<http://dbpedia.org/ontology/>\n",
+    "\n",
+    "# YOUR ANSWER HERE\n",
+    "\n",
+    "WHERE {\n",
+    "    ?escritor a dbo:Writer .\n",
+    "    ?escritor rdfs:label ?nombre .\n",
+    "    ?escritor dbo:birthDate ?fechaNac .\n",
+    "    ?escritor dbo:birthPlace dbr:Madrid .\n",
+    "    # YOUR ANSWER HERE\n",
+    "    FILTER(lang(?nombre) = \"es\") .\n",
+    "    FILTER(!bound(?titulo) || lang(?titulo) = \"en\") .\n",
+    "\n",
+    "}\n",
+    "ORDER BY ?nombre\n",
+    "LIMIT 100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## References"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "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",
+    "© 2018 Universidad Politécnica de Madrid."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

lod/helpers.py

@@ -12,6 +12,7 @@ from urllib.request import Request, urlopen
 from urllib.parse import quote_plus, urlencode
 from urllib.error import HTTPError
 
+import ssl
 import json
 import sys
 
@@ -20,7 +21,9 @@ display_javascript(js, raw=True)
 
 
 def send_query(query, endpoint):
-    FORMATS = ",".join(["application/sparql-results+json", "text/javascript", "application/json"])
+    FORMATS = ",".join(["application/sparql-results+json",
+                        "text/javascript",
+                        "application/json"])
 
     data = {'query': query}
     # b = quote_plus(query)
@@ -30,10 +33,18 @@ def send_query(query, endpoint):
                 headers={'content-type': 'application/x-www-form-urlencoded',
                          'accept': FORMATS},
                 method='POST')
-    res = urlopen(r)
+    context = ssl.create_default_context()
+    context.check_hostname = False
+    context.verify_mode = ssl.CERT_NONE
+
+    res = urlopen(r, context=context, timeout=2)
     data = res.read().decode('utf-8')
     if res.getcode() == 200:
-        return json.loads(data)
+        try:
+            return json.loads(data)
+        except Exception:
+            print('Got: ', data, file=sys.stderr)
+            raise
     raise Exception('Error getting results: {}'.format(data))
 
 
@@ -60,7 +71,7 @@ def solution():
 def query(query, endpoint=None, print_table=False):
     global LAST_QUERY
 
-    endpoint = endpoint or "http://fuseki.cluster.gsi.dit.upm.es/sitc/"
+    endpoint = endpoint or "http://fuseki.gsi.upm.es/sitc/"
     results = send_query(query, endpoint)
     tuples = to_table(results)
 

lod/tutorial/css/github.css

@@ -0,0 +1,61 @@
+.highlight { padding-top: 0; margin: 0;}
+.highlight .c { color: #999988; font-style: italic } /* Comment */
+.highlight .err { color: #a61717; background-color: #e3d2d2 } /* Error */
+.highlight .k { color: #000000; font-weight: bold } /* Keyword */
+.highlight .o { color: #000000; font-weight: bold } /* Operator */
+.highlight .cm { color: #999988; font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: #999999; font-weight: bold; font-style: italic } /* Comment.Preproc */
+.highlight .c1 { color: #999988; font-style: italic } /* Comment.Single */
+.highlight .cs { color: #999999; font-weight: bold; font-style: italic } /* Comment.Special */
+.highlight .gd { color: #000000; background-color: #ffdddd } /* Generic.Deleted */
+.highlight .ge { color: #000000; font-style: italic } /* Generic.Emph */
+.highlight .gr { color: #aa0000 } /* Generic.Error */
+.highlight .gh { color: #999999 } /* Generic.Heading */
+.highlight .gi { color: #000000; background-color: #ddffdd } /* Generic.Inserted */
+.highlight .go { color: #888888 } /* Generic.Output */
+.highlight .gp { color: #555555 } /* Generic.Prompt */
+.highlight .gs { font-weight: bold } /* Generic.Strong */
+.highlight .gu { color: #aaaaaa } /* Generic.Subheading */
+.highlight .gt { color: #aa0000 } /* Generic.Traceback */
+.highlight .kc { color: #000000; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #000000; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #000000; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #000000; font-weight: bold } /* Keyword.Pseudo */
+.highlight .kr { color: #000000; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #445588; font-weight: bold } /* Keyword.Type */
+.highlight .m { color: #009999 } /* Literal.Number */
+.highlight .s { color: #d01040 } /* Literal.String */
+.highlight .na { color: #008080 } /* Name.Attribute */
+.highlight .nb { color: #0086B3 } /* Name.Builtin */
+.highlight .nc { color: #445588; font-weight: bold } /* Name.Class */
+.highlight .no { color: #008080 } /* Name.Constant */
+.highlight .nd { color: #3c5d5d; font-weight: bold } /* Name.Decorator */
+.highlight .ni { color: #800080 } /* Name.Entity */
+.highlight .ne { color: #990000; font-weight: bold } /* Name.Exception */
+.highlight .nf { color: #990000; font-weight: bold } /* Name.Function */
+.highlight .nl { color: #990000; font-weight: bold } /* Name.Label */
+.highlight .nn { color: #555555 } /* Name.Namespace */
+.highlight .nt { color: #000080 } /* Name.Tag */
+.highlight .nv { color: #008080 } /* Name.Variable */
+.highlight .ow { color: #000000; font-weight: bold } /* Operator.Word */
+.highlight .w { color: #bbbbbb } /* Text.Whitespace */
+.highlight .mf { color: #009999 } /* Literal.Number.Float */
+.highlight .mh { color: #009999 } /* Literal.Number.Hex */
+.highlight .mi { color: #009999 } /* Literal.Number.Integer */
+.highlight .mo { color: #009999 } /* Literal.Number.Oct */
+.highlight .sb { color: #d01040 } /* Literal.String.Backtick */
+.highlight .sc { color: #d01040 } /* Literal.String.Char */
+.highlight .sd { color: #d01040 } /* Literal.String.Doc */
+.highlight .s2 { color: #d01040 } /* Literal.String.Double */
+.highlight .se { color: #d01040 } /* Literal.String.Escape */
+.highlight .sh { color: #d01040 } /* Literal.String.Heredoc */
+.highlight .si { color: #d01040 } /* Literal.String.Interpol */
+.highlight .sx { color: #d01040 } /* Literal.String.Other */
+.highlight .sr { color: #009926 } /* Literal.String.Regex */
+.highlight .s1 { color: #d01040 } /* Literal.String.Single */
+.highlight .ss { color: #990073 } /* Literal.String.Symbol */
+.highlight .bp { color: #999999 } /* Name.Builtin.Pseudo */
+.highlight .vc { color: #008080 } /* Name.Variable.Class */
+.highlight .vg { color: #008080 } /* Name.Variable.Global */
+.highlight .vi { color: #008080 } /* Name.Variable.Instance */
+.highlight .il { color: #009999 } /* Literal.Number.Integer.Long */

lod/tutorial/css/style.css

@@ -0,0 +1,968 @@
+@media screen {
+
+  body {
+    font-family: 'Quattrocento', Verdana, sans-serif;
+    font-size:16px;
+    background-color:#ffffff;
+  }
+
+  .container {
+    max-width: 48rem;
+    overflow: hidden;
+    text-overflow: ellipsis;
+  }
+
+
+/* =============================================================================
+Helper classes
+========================================================================== */
+
+  .noclear {
+    clear:none;
+  }
+
+  .expanded {
+    max-width: 58rem;
+  }
+
+  .garnish {
+    width: 23%;
+    padding:0;
+  }
+
+  .full-width {
+    width:80%;
+    margin: 0 auto;
+    text-align:center;
+  }
+
+  .float-right {
+    float:right;
+    margin-left: 1rem;
+    margin-bottom: 1rem;
+  }
+
+  .float-left {
+    margin-right: 1rem;
+    margin-bottom: 1rem;
+  }
+
+
+/* =============================================================================
+Home Page
+========================================================================== */
+
+  .home-block {
+    padding:3rem 0;
+    color:#666;
+  }
+
+  .home-block h2 {
+    margin:0;
+    font-size:2.8rem;
+    color:#333;
+    text-align:center;
+  }
+
+  .home-block p {
+    margin:0rem;
+    font-family:'Open Sans';
+    font-size:1.2rem;
+    padding-top:2rem;
+    text-align:justify;
+  }
+
+  .home-block a:visited {
+    color: #38c;
+  }
+
+  .home-stripe-1 {
+    color:#eee;
+    background:#27b;
+  }
+
+  .home-stripe-1 h2, .home-stripe-2 h2 {
+    color:#fff;
+  }
+
+  .home-stripe-1 a:visited, .home-stripe-1 a:link {
+    color:#6bf;
+  }
+
+  .home-stripe-2 {
+    color:#fff;
+    background:#289;
+  }
+
+  .home-stripe-2 a:visited, .home-stripe-2 a:link {
+    color:#6cd;
+  }
+
+  .home-image {
+    width: 75%;
+  }
+
+  .home-logo img {
+    width: 200px;
+  }
+
+  .home-logo a h1 {
+    color: #fff;
+  }
+
+  .home-logo {
+    color: #fff;
+  }
+
+  .home-logo li {
+    font-size: 1.2rem;
+  }
+
+  .en-back {
+    background-color: #444444;
+  }
+
+  .es-back {
+    background-color: #535D7F;
+  }
+
+  .fr-back {
+    background-color: #3D7C81;
+  }
+  
+  .pt-back {
+    background-color: #d6b664;
+  }
+
+
+  .sitewide-alert {
+    position: relative;
+    margin-bottom: 0;
+  }
+
+/* =============================================================================
+Lesson Headers
+========================================================================== */
+
+  header {
+    margin:-3rem 0 3rem 0;
+    padding:0;
+    font-family:'Roboto', sans-serif;
+    color:#ccc;
+    background: #efefef;
+    border-top:1px solid #333;
+    border-bottom:1px solid #333;
+    text-align:left;
+  }
+
+  header .container-fluid {
+    margin:0;
+    padding:1rem;
+    background: #f5f5f5;
+  }
+
+  header h1 {
+    margin:0;
+    padding:0;
+    font-size:1.8rem;
+    text-align:left;
+  }
+
+  header h2 {
+    font-family:'Roboto', sans-serif;
+    font-size:1.2rem;
+    color:#333;
+    margin: 1.5rem 0 1.5rem 0rem;
+    text-align:left;
+  }
+
+  header h3, header h4 {
+    font: .9rem/1.1rem 'Roboto Condensed', sans-serif;
+    text-transform:uppercase;
+    font-variant:small-caps;
+    letter-spacing:80%;
+    color:#666;
+    margin:.3rem 0 0 0;
+    padding:0;
+  }
+
+  header h4 {
+    display:inline;
+    margin:0;
+    line-height:1.3rem;
+  }
+
+  header .header-image {
+    float:left;
+    border:.2rem solid gray;
+    margin:0;
+    padding:0;
+    max-width: 200px;
+  }
+
+  header .header-abstract {
+    font: 1rem/1.4rem 'Roboto', sans-serif;
+    color:#666;
+    margin:1rem 0;
+  }
+
+  header .header-helpers {
+    clear:both;
+    background:#ccc;
+    color:#fff;
+    border-top:1px solid #999;
+    border-bottom:1px solid #999;
+  }
+
+  header ul {
+    margin:0;
+    padding:0;
+    list-style-type: none;
+  }
+
+  header li, header .metarow {
+    font: .9rem/1.1rem 'Roboto Condensed';
+  }
+
+  header .metarow {
+    color:#999;
+  }
+
+  header .peer-review, header .open-license {
+    font-size: 0.9rem;
+    color: #666;
+    margin: 0;
+  }
+
+/* =============================================================================
+Lessons Index
+========================================================================== */
+
+/*****************
+  FILTER BUTTONS
+******************/
+  ul.filter, ul.sort-by {
+    margin: 0 0 1rem 0;
+    padding: 0px;
+    text-align:center;
+  }
+
+  li.filter,
+  li.sort,
+  #filter-none {
+    font: .9rem/1.1rem 'Open Sans', sans-serif;
+    padding: .4rem .6rem;
+    border:none;
+    border-radius: 3px;
+    display:inline-block;
+    text-transform:uppercase;
+    text-decoration: none;
+  }
+
+  .filter li:hover,
+  .sort-by li:hover,
+  #filter-none:hover {
+    cursor: pointer;
+  }
+
+  .activities li.current:hover,
+  .filter li.current:hover,
+  .sort-by li.current:hover {
+    cursor:default;
+  }
+
+  .topic li a {
+    text-decoration: none;
+  }
+
+  .activities li {
+    background-color:#38c;
+    color:#fff;
+  }
+
+  .activities li:hover {
+    background-color:#16a;
+  }
+
+  .activities li.current {
+    background-color:#059;
+  }
+
+  .topics li {
+    background-color:#eee;
+    color: #38a;
+  }
+
+  .topics li:hover {
+    background-color:#ccc;
+  }
+
+  .topics li.current {
+    background-color:#aaa;
+    color: #333;
+  }
+
+
+  #filter-none {
+    width:99.5%;
+    clear:both;
+    text-align:center;
+    margin-bottom:1rem;
+    background-color:#fefefe;
+    color:#666;
+    border:1px solid #999;
+  }
+
+  #filter-none:hover {
+    background-color:#ededed;
+  }
+
+  /*****************
+    SEARCH
+  *****************/
+
+  .search-input {
+    width:55%;
+    clear:both;
+    margin-bottom:1rem;
+    background-color:#fefefe;
+    color:#666;
+    border:1px solid #999;
+    font: .9rem/1.1rem 'Open Sans',
+    sans-serif;
+    padding: .4rem .6rem;
+    border-radius: 3px;
+    display:inline-block;
+    text-transform:uppercase;
+    text-decoration: none;
+  }
+
+  #search-button,
+  #enable-search-button {
+    background-color: #efefef;
+    color: rgb(153, 143, 143);
+    width: 35%;
+    font: .9rem/1.1rem 'Open Sans',
+    sans-serif;
+    padding: .4rem .6rem;
+    border: none;
+    border-radius: 3px;
+    display: inline-block;
+    text-transform: uppercase;
+    text-decoration: none;
+  }
+
+  @media only screen and (max-width: 767px) {
+    /* phones */
+    #search-button,
+    #enable-search-button {
+      width: 80%;
+    }
+  }
+
+
+  #search-info-button {
+    padding: 0.5rem;
+    color: rgb(153, 143, 143);
+  }
+
+  #search-info {
+    display: none;
+    height:0px;
+    background:#efefef;
+    overflow:hidden;
+    transition:0.5s;
+    -webkit-transition:0.5s;
+    width: 100%;
+    text-align: left;
+    box-sizing: border-box;
+  }
+
+  #search-info.visible {
+    display: block;
+    height: fit-content;
+    height: -moz-max-content;
+    padding: 10px;
+    margin-top: 10px;
+  }
+
+  /*****************
+    SORT BUTTONS
+  *****************/
+
+  li.sort {
+    background-color: #efefef;
+    color:#666;
+    width:49.5%;
+  }
+
+  li.sort:hover {
+    text-decoration: none;
+    background-color:#cecece;
+  }
+
+  #current-sort {
+    font-size:75%;
+  }
+
+  .sort.my-desc:after, .sort-desc:after {
+    width: 0;
+    height: 0;
+    border-left: .4rem solid transparent;
+    border-right: .4rem solid transparent;
+    border-top: .4rem solid;
+    content:"";
+    position: relative;
+    top:.75rem;
+    right:-.3rem;
+  }
+
+  .sort.my-asc:after, .sort-asc:after {
+    width: 0;
+    height: 0;
+    border-left: .4rem solid transparent;
+    border-right: .4rem solid transparent;
+    border-bottom: .4rem solid;
+    content:"";
+    position: relative;
+    bottom:.75rem;
+    right:-.3rem;
+  }
+
+  .sort-desc:after {
+    top:1rem;
+  }
+
+  .sort-asc:after {
+    bottom:1rem;
+  }
+
+  /*****************************
+    LESSON INDEX RESULTS LIST
+  *****************************/
+
+   h2.results-title {
+     margin:1rem 0;
+     font: 1.6rem/2rem 'Roboto Condensed';
+     color:#666;
+     text-transform:uppercase;
+   }
+
+   #results-value {
+     color:#000;
+   }
+
+
+  #lesson-list .list ul {
+    margin:0;
+    padding:0;
+  }
+
+  #lesson-list .list li {
+    list-style-type:none;
+    margin:0;
+  }
+
+
+  .lesson-description {
+    margin-bottom:2rem;
+    padding:0rem;
+    min-height:120px;
+    text-align:left;
+  }
+
+  .lesson-description img {
+      width:100%;
+  }
+
+  .lesson-image {
+    width:120px;
+    float:left;
+    margin-right:1rem;
+  }
+
+  .above-title {
+    margin:0 0 .2rem 0;
+    font: .8rem/1rem 'Roboto Condensed';
+    color:#999;
+    text-transform:uppercase;
+    clear:none;
+  }
+
+  .lesson-description h2.title {
+    font: 1.2rem/1.3rem 'Crete Round', serif;
+    margin:0 0 .8rem 0;
+    clear:none;
+  }
+
+  .list .date,
+  .lesson-description .activity,
+  .lesson-description .topics,
+  .lesson-description .difficulty {
+    display: none;
+  }
+
+  #pre-loader {
+    visibility: hidden;
+    display: flex;
+    justify-content: center;
+    align-items: center;
+    height: 100vh;
+    width: 100%;
+    position: fixed;
+    top: 0;
+    left: 0;
+    z-index: 9999;
+    transition: opacity 0.3s linear;
+    background: rgba(211, 211, 211, 0.8);
+  }
+/* =============================================================================
+Top Navigation Bar
+========================================================================== */
+
+  .navbar {
+    padding: .6rem 1rem;
+    margin: 0 0 3rem 0;
+  }
+
+  .navbar-dark .navbar-nav .nav-link {
+    font-family:'Open Sans';
+    text-transform:uppercase;
+    color:#fff;
+    font-size:.9rem;
+  }
+
+  .btn-group > .btn-secondary {
+    border-color: #333333;
+    background-color: #888888;
+  }
+
+  .lang {
+    text-transform:lowercase !important;
+  }
+
+  .navbar-dark .navbar-nav .nav-link:hover, .navbar-dark .navbar-brand:hover {
+    color:#39a;
+  }
+
+  .navbar-toggler-icon {
+        background-image: url("data:image/svg+xml;charset=utf8,%3Csvg viewBox='0 0 32 32' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath stroke='rgba(255,255,255, 1)' stroke-width='2' stroke-linecap='round' stroke-miterlimit='10' d='M4 8h24M4 16h24M4 24h24'/%3E%3C/svg%3E");
+  }
+
+  .navbar-collapse {
+    text-align:center;
+  }
+
+  .navbar-dark .navbar-brand {
+    font-family:'Crete Round', serif;
+    color:#fff;
+    letter-spacing: .02em;
+  }
+
+  .btn-group > a.btn {
+    padding-left: 1rem;
+    padding-right: 1rem;
+  }
+
+  a.dropdown-item {
+    border-bottom:1px solid #ccc;
+    font-family:'Roboto';
+  }
+
+  .dropdown-menu {
+  	position: absolute;
+  	background: #fff;
+  	border: 1px solid #ccc;
+    margin:0;
+    padding:0;
+  }
+
+  .dropdown-menu a {
+  	font-size:.8rem;
+    line-height:2rem;
+    text-transform:uppercase;
+  }
+
+  .dropdown-menu a:last-child {
+    border-bottom:none;
+  }
+
+  .dropdown-menu:after, .dropdown-menu:before {
+  	bottom: 100%;
+  	left: 20%;
+  	border: solid transparent;
+  	content: " ";
+  	height: 0;
+  	width: 0;
+  	position: absolute;
+  	pointer-events: none;
+  }
+
+  .dropdown-menu:after {
+  	border-color: rgba(255, 255, 255, 0);
+  	border-bottom-color: #fff;
+  	border-width: 12px;
+  	margin-left: -12px;
+  }
+  .dropdown-menu:before {
+  	border-color: rgba(51, 153, 170, 0);
+  	border-bottom-color: #ccc;
+  	border-width: 13px;
+  	margin-left: -13px;
+  }
+
+  .navbar-dark .navbar-nav .nav-link:focus {
+    color: #ccc;
+  }
+
+  .header-link {
+    position: absolute;
+    right: 0.6em;
+    opacity: 0;
+    -webkit-transition: opacity 0.2s ease-in-out 0.1s;
+    -moz-transition: opacity 0.2s ease-in-out 0.1s;
+    -ms-transition: opacity 0.2s ease-in-out 0.1s;
+  }
+
+  h2:hover .header-link,
+  h3:hover .header-link,
+  h4:hover .header-link,
+  h5:hover .header-link,
+  h6:hover .header-link {
+    opacity: 1;
+  }
+
+/* =============================================================================
+Lesson Typography
+========================================================================== */
+
+  a {text-decoration:none;}
+
+  a:link {color: #38c;}
+  a:visited {color: #39a;}
+  a:hover {color: #555;}
+  a:active {color: #555;}
+
+  b, strong { font-weight: bold; }
+
+  blockquote { margin: 1em 2em; padding: 0 1em 0 1em; font-style: italic; border:1px solid #666; background: #eeeeee;}
+
+  hr {
+    display: block; height: 1px; border: 0; border-top: 1px solid #ccc; margin: 2em 0; padding: 0; }
+
+  img {
+    max-width:100%;
+  }
+
+  ins { background: #ff9; color: #000; text-decoration: none; }
+
+
+  h1,h2,h3,h4,h5 {
+    font-family:'Crete Round', serif;
+    font-weight:normal;
+    clear:both;
+  }
+
+
+  h1 {
+    font-size:2rem;
+    margin-bottom:1.5rem;
+    letter-spacing:-.03rem;
+    text-align:center;
+  }
+
+  h2 {
+    font-size:1.6rem;
+    margin-top:3rem;
+    letter-spacing:-.02rem;
+  }
+
+  h3 {
+    font-size:1.4rem;
+    margin-top:2.5rem;
+  }
+
+  h4 {
+    font-size:1.2rem;
+    margin-top:1.8rem;
+  }
+
+  h5 {
+    font-size:1.0rem;
+    margin-top:1.4rem;
+  }
+
+  h1 a, h2 a, h3 a, h4 a, h5 a {
+    text-decoration:none;
+  }
+
+  h1 a:link { color: #38c; }
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+<!-- concon&#45;&gt;conlab -->
+<g id="edge9" class="edge"><title>concon&#45;&gt;conlab</title>
+<path fill="none" stroke="gray" d="M576.27,-79.6893C576.27,-69.8938 576.27,-57.4218 576.27,-46.335"/>
+<polygon fill="gray" stroke="gray" points="579.77,-46.2623 576.27,-36.2623 572.77,-46.2624 579.77,-46.2623"/>
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+</g>
+</g>
+</svg>

lod/tutorial/images/graph-databases-and-SPARQL/sparql07.svg

@@ -0,0 +1,114 @@
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN"
+ "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<!-- Generated by graphviz version 2.38.0 (20140413.2041)
+ -->
+<!-- Title: %3 Pages: 1 -->
+<svg width="367pt" height="364pt"
+ viewBox="0.00 0.00 367.21 364.00" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
+<g id="graph0" class="graph" transform="scale(1 1) rotate(0) translate(4 360)">
+<title>%3</title>
+<polygon fill="white" stroke="none" points="-4,4 -4,-360 363.215,-360 363.215,4 -4,4"/>
+<!-- obj -->
+<g id="node1" class="node"><title>obj</title>
+<ellipse fill="none" stroke="gray" cx="148.735" cy="-338" rx="148.97" ry="18"/>
+<text text-anchor="middle" x="148.735" y="-335" font-family="Helvetica,sans-Serif" font-size="10.00">http://collection.britishmuseum.org/id/object/PPA82633</text>
+</g>
+<!-- object_type -->
+<g id="node2" class="node"><title>object_type</title>
+<ellipse fill="none" stroke="gray" cx="51.7348" cy="-258" rx="38.5366" ry="18"/>
+<text text-anchor="middle" x="51.7348" y="-255" font-family="Helvetica,sans-Serif" font-size="10.00">object_type</text>
+</g>
+<!-- obj&#45;&gt;object_type -->
+<g id="edge1" class="edge"><title>obj&#45;&gt;object_type</title>
+<path fill="none" stroke="gray" d="M98.3951,-320.855C88.4182,-315.964 78.6502,-309.764 70.9106,-302 66.4004,-297.476 62.8568,-291.706 60.1099,-285.873"/>
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+</g>
+<!-- production -->
+<g id="node4" class="node"><title>production</title>
+<ellipse fill="none" stroke="gray" cx="148.735" cy="-258" rx="36.3999" ry="18"/>
+<text text-anchor="middle" x="148.735" y="-255" font-family="Helvetica,sans-Serif" font-size="10.00">production</text>
+</g>
+<!-- obj&#45;&gt;production -->
+<g id="edge3" class="edge"><title>obj&#45;&gt;production</title>
+<path fill="none" stroke="gray" d="M148.735,-319.689C148.735,-309.894 148.735,-297.422 148.735,-286.335"/>
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+</g>
+<!-- other -->
+<g id="node8" class="node"><title>other</title>
+<text text-anchor="middle" x="281.735" y="-255" font-family="Helvetica,sans-Serif" font-size="10.00">Other top&#45;level object attributes</text>
+</g>
+<!-- obj&#45;&gt;other -->
+<g id="edge7" class="edge"><title>obj&#45;&gt;other</title>
+<path fill="none" stroke="gray" d="M223.709,-322.337C236.677,-317.399 249.318,-310.804 259.735,-302 264.947,-297.595 269.068,-291.646 272.265,-285.579"/>
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+</g>
+<!-- print -->
+<g id="node3" class="node"><title>print</title>
+<ellipse fill="none" stroke="gray" cx="51.7348" cy="-178" rx="27" ry="18"/>
+<text text-anchor="middle" x="51.7348" y="-175" font-family="Helvetica,sans-Serif" font-size="10.00">print</text>
+</g>
+<!-- object_type&#45;&gt;print -->
+<g id="edge2" class="edge"><title>object_type&#45;&gt;print</title>
+<path fill="none" stroke="gray" d="M51.7348,-239.689C51.7348,-229.894 51.7348,-217.422 51.7348,-206.335"/>
+<polygon fill="gray" stroke="gray" points="55.2349,-206.262 51.7348,-196.262 48.2349,-206.262 55.2349,-206.262"/>
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+</g>
+<!-- date -->
+<g id="node5" class="node"><title>date</title>
+<ellipse fill="none" stroke="gray" cx="134.735" cy="-178" rx="27" ry="18"/>
+<text text-anchor="middle" x="134.735" y="-175" font-family="Helvetica,sans-Serif" font-size="10.00">date</text>
+</g>
+<!-- production&#45;&gt;date -->
+<g id="edge4" class="edge"><title>production&#45;&gt;date</title>
+<path fill="none" stroke="gray" d="M143.042,-240.075C141.328,-234.389 139.605,-227.974 138.481,-222 137.543,-217.015 136.839,-211.66 136.311,-206.48"/>
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+</g>
+<!-- other_prod -->
+<g id="node9" class="node"><title>other_prod</title>
+<text text-anchor="middle" x="234.735" y="-175" font-family="Helvetica,sans-Serif" font-size="10.00">Other production info</text>
+</g>
+<!-- production&#45;&gt;other_prod -->
+<g id="edge8" class="edge"><title>production&#45;&gt;other_prod</title>
+<path fill="none" stroke="gray" d="M176.351,-246.343C188.633,-240.562 202.579,-232.439 212.735,-222 217.34,-217.267 221.193,-211.376 224.322,-205.489"/>
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+</g>
+<!-- timespan -->
+<g id="node6" class="node"><title>timespan</title>
+<ellipse fill="none" stroke="gray" cx="134.735" cy="-98" rx="32.8294" ry="18"/>
+<text text-anchor="middle" x="134.735" y="-95" font-family="Helvetica,sans-Serif" font-size="10.00">timespan</text>
+</g>
+<!-- date&#45;&gt;timespan -->
+<g id="edge5" class="edge"><title>date&#45;&gt;timespan</title>
+<path fill="none" stroke="gray" d="M134.735,-159.689C134.735,-149.894 134.735,-137.422 134.735,-126.335"/>
+<polygon fill="gray" stroke="gray" points="138.235,-126.262 134.735,-116.262 131.235,-126.262 138.235,-126.262"/>
+<text text-anchor="middle" x="178.088" y="-135.6" font-family="Helvetica,sans-Serif" font-size="8.00">ecrm:P4_has_time&#45;span</text>
+</g>
+<!-- start_date -->
+<g id="node7" class="node"><title>start_date</title>
+<ellipse fill="none" stroke="gray" cx="66.7348" cy="-18" rx="34.828" ry="18"/>
+<text text-anchor="middle" x="66.7348" y="-15" font-family="Helvetica,sans-Serif" font-size="10.00">start_date</text>
+</g>
+<!-- timespan&#45;&gt;start_date -->
+<g id="edge6" class="edge"><title>timespan&#45;&gt;start_date</title>
+<path fill="none" stroke="gray" d="M105.598,-89.5265C91.4682,-84.285 75.76,-75.6942 67.3129,-62 64.4467,-57.3534 63.1708,-51.8529 62.8105,-46.3654"/>
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+<text text-anchor="middle" x="124.446" y="-55.6" font-family="Helvetica,sans-Serif" font-size="8.00">ecrm:P82a_begin_of_the_begin</text>
+</g>
+<!-- other_date -->
+<g id="node10" class="node"><title>other_date</title>
+<text text-anchor="middle" x="202.735" y="-15" font-family="Helvetica,sans-Serif" font-size="10.00">End of begin, Start of end...</text>
+</g>
+<!-- timespan&#45;&gt;other_date -->
+<g id="edge9" class="edge"><title>timespan&#45;&gt;other_date</title>
+<path fill="none" stroke="gray" d="M155.643,-84.0943C164.163,-78.1192 173.652,-70.4665 180.735,-62 184.802,-57.138 188.386,-51.398 191.417,-45.7224"/>
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+</g>
+</g>
+</svg>

lod/tutorial/js/bootstrap-4-navbar.js

@@ -0,0 +1,34 @@
+
+/*!
+ * Bootstrap 4 multi dropdown navbar ( https://bootstrapthemes.co/demo/resource/bootstrap-4-multi-dropdown-navbar/ )
+ * Copyright 2017.
+ * Licensed under the GPL license
+ */
+
+
+$( document ).ready( function () {
+    $( '.mobile-drop a.dropdown-toggle' ).on( 'click', function ( e ) {
+        var $el = $( this );
+        var $parent = $( this ).offsetParent( ".mobile-drop" );
+        if ($('.show.mobile-drop').length > 0){
+          $('.show.mobile-drop').each(function(item){
+            $(this).toggleClass('show');
+          });
+        }
+
+        var $subMenu = $( this ).next( ".mobile-drop" );
+        $subMenu.toggleClass( 'show' );
+
+        $( this ).parent( "li" ).toggleClass( 'show' );
+
+        $( this ).parents( 'li.nav-item.dropdown.mobile-drop.show' ).on( 'click', function ( e ) {
+            $( '.mobile-drop .show' ).removeClass( "show" );
+        } );
+
+         if ( !$parent.parent().hasClass( 'navbar-nav' ) ) {
+            $el.next().css( { "top": $el[0].offsetTop, "left": $parent.outerWidth() - 4 } );
+        }
+
+        return false;
+    } );
+} );

lod/tutorial/js/ext_links.js

@@ -0,0 +1,8 @@
+$(document).ready(function() {
+   $('a').each(function() {
+      var a = new RegExp('/' + window.location.host + '/');
+      if (!a.test(this.href)) {
+      $(this).attr("target","_blank");
+      }
+   });
+});

lod/tutorial/js/header_links.js

@@ -0,0 +1,13 @@
+// http://ben.balter.com/2014/03/13/pages-anchor-links/
+
+$(function() {
+  return $("h2, h3, h4, h5, h6").each(function(i, el) {
+    var $el, icon, id;
+    $el = $(el);
+    id = $el.attr('id');
+    icon = '<i class="fa fa-link" style="font-size: 0.8em"></i>';
+    if (id) {
+      return $el.append($("<a />").addClass("header-link").attr("href", "#" + id).html(icon));
+    }
+  });
+});

lod/upload.sh

@@ -0,0 +1,53 @@
+#!/bin/sh
+
+# This is a bit messy
+if [ "$#" -lt 1 ]; then
+        graph="http://example.com/sitc/submission/"
+        endpoint="http://fuseki.gsi.upm.es/hotels/data"
+else if [ "$#" -lt 2 ]; then
+        endpoint=$1
+        graph_base="http://example.com/sitc"
+     else
+         if [ "$#" -lt 3 ]; then
+             endpoint=$1
+             graph=$2
+         else
+             echo "Usage: $0 [<endpoint>] [<graph_base_uri>]"
+             echo
+             exit 1
+         fi
+     fi
+fi
+
+
+upload(){
+    name=$1
+    file=$2
+    echo '###'
+    echo "Uploading: $graph"
+    echo "Graph: $graph"
+    echo "Endpoint: $endpoint"
+    curl -X POST \
+         --digest -u admin:$PASSWORD \
+         -H Content-Type:text/turtle \
+         -T "$file" \
+         --data-urlencode graph=$graph_base/$name \
+	 -G $endpoint 
+
+}
+
+
+total=0
+echo -n "Password: "
+read -s PASSWORD
+
+echo "Uploading synthethic"
+upload "synthetic" synthetic/reviews.ttl || exit 1
+
+for i in *.ttl; do
+    identifier=$(echo ${i%.ttl} | md5sum | awk '{print $1}')
+    echo "Uploading $i"
+    upload $identifier $i 
+    total=$((total + 1))
+done
+echo Uploaded $total

ml1/2_0_0_Intro_ML.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![](files/images/EscUpmPolit_p.gif \"UPM\")"
+    "![](./images/EscUpmPolit_p.gif \"UPM\")"
    ]
   },
   {
@@ -71,8 +71,7 @@
    "source": [
     "* [Scikit-learn web page](http://scikit-learn.org/stable/)\n",
     "* [Scikit-learn videos](http://blog.kaggle.com/author/kevin-markham/) and [notebooks](https://github.com/justmarkham/scikit-learn-videos) by Kevin Marham\n",
-    "* [Learning scikit-learn: Machine Learning in Python](http://proquest.safaribooksonline.com/book/programming/python/9781783281930/1dot-machine-learning-a-gentle-introduction/ch01s02_html), Raúl Garreta; Guillermo Moncecchi, Packt Publishing, 2013.\n",
+    "* [Python Machine Learning](https://learning.oreilly.com/library/view/python-machine-learning/9781789955750/), Sebastian Raschka, Packt Publishing, 2019."
-    "* [Python Machine Learning](http://proquest.safaribooksonline.com/book/programming/python/9781783555130), Sebastian Raschka, Packt Publishing, 2015."
    ]
   },
   {
@@ -80,7 +79,7 @@
    "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",
+    "The notebook is freely licensed under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/).  \n",
     "\n",
     "©  Carlos A. Iglesias, Universidad Politécnica de Madrid."
    ]
@@ -88,7 +87,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -102,7 +101,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.7"
+   "version": "3.8.12"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,

ml1/2_0_1_Objectives.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![](files/images/EscUpmPolit_p.gif \"UPM\")"
+    "![](./images/EscUpmPolit_p.gif \"UPM\")"
    ]
   },
   {
@@ -40,10 +40,10 @@
     "\n",
     "* Learn to use scikit-learn\n",
     "* Learn the basic steps to apply machine learning techniques: dataset analysis, load, preprocessing, training, validation, optimization and persistence.\n",
-    "* Learn how to do a exploratory data analysis\n",
+    "* Learn how to do an exploratory data analysis\n",
     "* Learn how to visualise a dataset\n",
     "* Learn how to load a bundled dataset\n",
-    "* Learn how to separate the dataset into traning and testing datasets\n",
+    "* Learn how to separate the dataset into training and testing datasets\n",
     "* Learn how to train a classifier\n",
     "* Learn how to predict with a trained classifier\n",
     "* Learn how to evaluate the predictions\n",
@@ -63,9 +63,7 @@
    "metadata": {},
    "source": [
     "* [Scikit-learn web page](http://scikit-learn.org/stable/)\n",
-    "* [Scikit-learn videos](http://blog.kaggle.com/author/kevin-markham/) and [notebooks](https://github.com/justmarkham/scikit-learn-videos) by Kevin Marham\n",
+    "* [Scikit-learn videos](http://blog.kaggle.com/author/kevin-markham/) and [notebooks](https://github.com/justmarkham/scikit-learn-videos) by Kevin Marham\n"
-    "* [Learning scikit-learn: Machine Learning in Python](http://proquest.safaribooksonline.com/book/programming/python/9781783281930/1dot-machine-learning-a-gentle-introduction/ch01s02_html), Raúl Garreta; Guillermo Moncecchi, Packt Publishing, 2013.\n",
-    "* [Python Machine Learning](http://proquest.safaribooksonline.com/book/programming/python/9781783555130), Sebastian Raschka, Packt Publishing, 2015."
    ]
   },
   {
@@ -73,7 +71,7 @@
    "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",
+    "The notebook is freely licensed under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/).  \n",
     "\n",
     "©  Carlos A. Iglesias, Universidad Politécnica de Madrid."
    ]
@@ -81,7 +79,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -95,7 +93,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.7"
+   "version": "3.8.12"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,

ml1/2_1_Intro_ScikitLearn.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![](files/images/EscUpmPolit_p.gif \"UPM\")"
+    "![](./images/EscUpmPolit_p.gif \"UPM\")"
    ]
   },
   {
@@ -87,10 +87,10 @@
    "metadata": {},
    "source": [
     "Scikit-learn provides algorithms for solving the following problems:\n",
-    "* **Classification**: Identifying to which category an object belongs to. Some of the available [classification algorithms](http://scikit-learn.org/stable/supervised_learning.html#supervised-learning) are decision trees (ID3, kNN, ...), SVM, Random forest, Perceptron, etc. \n",
+    "* **Classification**: Identifying to which category an object belongs. Some of the available [classification algorithms](http://scikit-learn.org/stable/supervised_learning.html#supervised-learning) are decision trees (ID3, C4.5, ...), kNN, SVM, Random forest, Perceptron, etc. \n",
     "* **Clustering**: Automatic grouping of similar objects into sets. Some of the available [clustering algorithms](http://scikit-learn.org/stable/modules/clustering.html#clustering) are k-Means, Affinity propagation, etc.\n",
     "* **Regression**: Predicting a continuous-valued attribute associated with an object. Some of the available [regression algorithms](http://scikit-learn.org/stable/supervised_learning.html#supervised-learning) are linear regression, logistic regression, etc.\n",
-    "* ** Dimensionality reduction**: Reducing the number of random variables to consider. Some of the available [dimensionality reduction algorithms](http://scikit-learn.org/stable/modules/decomposition.html#decompositions) are SVD, PCA, etc."
+    "* **Dimensionality reduction**: Reducing the number of random variables to consider. Some of the available [dimensionality reduction algorithms](http://scikit-learn.org/stable/modules/decomposition.html#decompositions) are SVD, PCA, etc."
    ]
   },
   {
@@ -105,7 +105,7 @@
    "metadata": {},
    "source": [
     "In addition, scikit-learn helps in several tasks:\n",
-    "* **Model selection**: Comparing, validating, choosing parameters and models, and persisting models. Some of the [available functionalities](http://scikit-learn.org/stable/model_selection.html#model-selection) are cross-validation or grid search for optimizing the parameters. \n",
+    "* **Model selection**: Comparing, validating, choosing parameters and models, and persisting models. Some [available functionalities](http://scikit-learn.org/stable/model_selection.html#model-selection) are cross-validation or grid search for optimizing the parameters. \n",
     "* **Preprocessing**: Several common utility functions and transformer classes to change raw feature vectors into a representation that is more suitable for the downstream estimators. Some of the available [preprocessing functions](http://scikit-learn.org/stable/modules/preprocessing.html#preprocessing) are scaling and normalizing data, or imputing missing values."
    ]
   },
@@ -128,9 +128,9 @@
     "\n",
     "If it is not installed, install it with conda: `conda install scikit-learn`.\n",
     "\n",
-    "If you have installed scipy and numpy, you can also installed using pip: `pip install -U scikit-learn`.\n",
+    "If you have installed scipy and numpy, you can also install using pip: `pip install -U scikit-learn`.\n",
     "\n",
-    "It is not recommended to use pip for installing scipy and numpy. Instead, use conda or install the linux package *python-sklearn*."
+    "It is not recommended to use pip to install scipy and numpy. Instead, use conda or install the Linux package *python-sklearn*."
    ]
   },
   {
@@ -156,7 +156,7 @@
    "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",
+    "The notebook is freely licensed under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/).  \n",
     "\n",
     "© Carlos A. Iglesias, Universidad Politécnica de Madrid."
    ]

ml1/2_2_Read_Data.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![](files/images/EscUpmPolit_p.gif \"UPM\")\n",
+    "![](./images/EscUpmPolit_p.gif \"UPM\")\n",
     "\n",
     "# Course Notes for Learning Intelligent Systems\n",
     "\n",
@@ -34,11 +34,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The goal of this notebook is to learn how to read and load a sample dataset.\n",
+    "This notebook aims to learn how to read and load a sample dataset.\n",
     "\n",
-    "Scikit-learn comes with some bundled [datasets](http://scikit-learn.org/stable/datasets/): iris, digits, boston, etc.\n",
+    "Scikit-learn comes with some bundled [datasets](https://scikit-learn.org/stable/datasets.html): iris, digits, boston, etc.\n",
     "\n",
-    "In this notebook we are going to use the Iris dataset."
+    "In this notebook, we will use the Iris dataset."
    ]
   },
   {
@@ -54,16 +54,25 @@
    "source": [
     "The [Iris flower dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set), available at [UCI dataset repository](https://archive.ics.uci.edu/ml/datasets/Iris), is a classic dataset for classification.\n",
     "\n",
-    "The dataset consists of 50 samples from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals, in centimetres. Based on the combination of these four features.\n",
+    "The dataset consists of 50 samples from each of three species of Iris (Iris setosa, Iris virginica, and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals, in centimetres. Based on the combination of these four features, a machine learning model will learn to differentiate the species of Iris.\n",
     "\n",
-    "![Iris](files/images/iris-dataset.jpg)"
+     "![Iris dataset](./images/iris-dataset.jpg \"Iris\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In ordert to read the dataset, we import the datasets bundle and then load the Iris dataset. "
+    "Here you can see the species and the features.\n",
+    "![Iris features](./images/iris-features.png \"Iris features\")\n",
+    "![Iris classes](./images/iris-classes.png \"Iris classes\")"
+   ]
+  },
+   {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To read the dataset, we import the datasets bundle and then load the Iris dataset. "
    ]
   },
   {
@@ -180,7 +189,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#Using numpy, I can print the dimensions (here we are working with 2D matriz)\n",
+    "#Using numpy, I can print the dimensions (here we are working with a 2D matrix)\n",
     "print(iris.data.ndim)"
    ]
   },
@@ -218,7 +227,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In following sessions we will learn how to load a dataset from a file (csv, excel, ...) using the pandas library."
+    "In the following sessions, we will learn how to load a dataset from a file (CSV, Excel, ...) using the pandas library."
    ]
   },
   {
@@ -246,7 +255,7 @@
    "source": [
     "## Licence\n",
     "\n",
-    "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/).  \n",
+    "The notebook is freely licensed under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/).  \n",
     "\n",
     "©  Carlos A. Iglesias, Universidad Politécnica de Madrid."
    ]

ml1/2_3_0_Visualisation.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![](files/images/EscUpmPolit_p.gif \"UPM\")"
+    "![](./images/EscUpmPolit_p.gif \"UPM\")"
    ]
   },
   {
@@ -49,7 +49,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The goal of this notebook is to learn how to analyse a dataset. We will cover other tasks such as cleaning or munging (changing the format) the dataset  in other sessions."
+    "This notebook aims to learn how to analyse a dataset. We will cover other tasks such as cleaning or munging (changing the format) the dataset  in other sessions."
    ]
   },
   {
@@ -65,13 +65,13 @@
    "source": [
     "This section covers different ways to inspect the distribution of samples per feature.\n",
     "\n",
-    "First of all, let's see how many samples of each class we have, using a [histogram](https://en.wikipedia.org/wiki/Histogram). \n",
+    "First of all, let's see how many samples we have in each class using a [histogram](https://en.wikipedia.org/wiki/Histogram). \n",
     "\n",
-    "A histogram is a graphical representation of the distribution of numerical data. It is an estimation of the probability distribution of a continuous variable (quantitative variable). \n",
+    "A histogram is a graphical representation of the distribution of numerical data. It estimates the probability distribution of a continuous variable (quantitative variable). \n",
     "\n",
-    "For building a histogram, we need first to 'bin' the range of values—that is, divide the entire range of values into a series of intervals—and then count how many values fall into each interval. \n",
+    "For building a histogram, we need to 'bin' the range of values—that is, divide the entire range of values into a series of intervals—and then count how many values fall into each interval. \n",
     "\n",
-    "In our case, since the values are not continuous and we have only three values, we do not need to bin them."
+    "Since the values are not continuous and we have only three values, we do not need to bin them."
    ]
   },
   {
@@ -115,7 +115,7 @@
    "metadata": {},
    "source": [
     "As can be seen, we have the same distribution of samples for every class.\n",
-    "The next step is to see the distribution of the features"
+    "The next step is to see the distribution of the features."
    ]
   },
   {
@@ -184,7 +184,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As we can see, the Setosa class seems to be linearly separable with these two features.\n",
+    "As we can see, the Setosa class seems linearly separable with these two features.\n",
     "\n",
     "Another nice visualisation is given below."
    ]
@@ -228,7 +228,6 @@
    "source": [
     "* [Feature selection](http://scikit-learn.org/stable/modules/feature_selection.html)\n",
     "* [Classification probability](http://scikit-learn.org/stable/auto_examples/classification/plot_classification_probability.html)\n",
-    "* [Mastering Pandas](http://proquest.safaribooksonline.com/book/programming/python/9781783981960), Femi Anthony, Packt Publishing, 2015.\n",
     "* [Matplotlib web page](http://matplotlib.org/index.html)\n",
     "* [Using matlibplot in IPython](http://ipython.readthedocs.org/en/stable/interactive/plotting.html)\n",
     "* [Seaborn Tutorial](https://stanford.edu/~mwaskom/software/seaborn/tutorial.html)\n",
@@ -242,7 +241,7 @@
    "source": [
     "## Licence\n",
     "\n",
-    "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/).  \n",
+    "The notebook is freely licensed under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/).  \n",
     "\n",
     "© Carlos A. Iglesias, Universidad Politécnica de Madrid."
    ]

ml1/2_4_Preprocessing.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![](files/images/EscUpmPolit_p.gif \"UPM\")"
+    "![](./images/EscUpmPolit_p.gif \"UPM\")"
    ]
   },
   {
@@ -76,7 +76,7 @@
    "source": [
     "A common practice in machine learning to evaluate an algorithm is to split the data at hand into two sets, one that we call the **training set** on which we learn data properties and one that we call the **testing set** on which we test these properties. \n",
     "\n",
-    "We are going to use *scikit-learn* to split the data into random training and testing sets. We follow the ratio 75% for training and 25% for testing. We use `random_state` to ensure that the result is always the same and it is reproducible. (Otherwise, we would get different training and testing sets every time)."
+    "We will use *scikit-learn* to split the data into random training and testing sets. We follow the ratio 75% for training and 25% for testing. We use `random_state` to ensure that the result is always the same and it is reproducible. (Otherwise, we would get different training and testing sets every time)."
    ]
   },
   {
@@ -122,9 +122,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Standardization of datasets is a common requirement for many machine learning estimators implemented in the scikit; they might behave badly if the individual features do not more or less look like standard normally distributed data: Gaussian with zero mean and unit variance.\n",
+    "Standardization of datasets is a common requirement for many machine learning estimators implemented in the scikit; they might misbehave if the individual features do not more or less look like standard normally distributed data: Gaussian with zero mean and unit variance.\n",
     "\n",
-    "The preprocessing module further provides a utility class `StandardScaler` to compute the mean and standard deviation on a training set. Later, the same transformation will be applied on the testing set."
+    "The preprocessing module further provides a utility class `StandardScaler` to compute a training set's mean and standard deviation. Later, the same transformation will be applied on the testing set."
    ]
   },
   {
@@ -163,7 +163,6 @@
    "source": [
     "* [Feature selection](http://scikit-learn.org/stable/modules/feature_selection.html)\n",
     "* [Classification probability](http://scikit-learn.org/stable/auto_examples/classification/plot_classification_probability.html)\n",
-    "* [Mastering Pandas](http://proquest.safaribooksonline.com/book/programming/python/9781783981960), Femi Anthony, Packt Publishing, 2015.\n",
     "* [Matplotlib web page](http://matplotlib.org/index.html)\n",
     "* [Using matlibplot in IPython](http://ipython.readthedocs.org/en/stable/interactive/plotting.html)\n",
     "* [Seaborn Tutorial](https://stanford.edu/~mwaskom/software/seaborn/tutorial.html)"
@@ -174,7 +173,7 @@
    "metadata": {},
    "source": [
     "### Licences\n",
-    "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/).  \n",
+    "The notebook is freely licensed under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/).  \n",
     "\n",
     "© Carlos A. Iglesias, Universidad Politécnica de Madrid."
    ]

ml1/2_5_0_Machine_Learning.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![](files/images/EscUpmPolit_p.gif \"UPM\")"
+    "![](./images/EscUpmPolit_p.gif \"UPM\")"
    ]
   },
   {
@@ -53,9 +53,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This is an introduction of general ideas about machine learning and the interface of scikit-learn, taken from the [scikit-learn tutorial](http://www.astroml.org/sklearn_tutorial/general_concepts.html). \n",
+    "This is an introduction to general ideas about machine learning and the interface of scikit-learn, taken from the [scikit-learn tutorial](http://www.astroml.org/sklearn_tutorial/general_concepts.html). \n",
     "\n",
-    "You can skip it during the lab session and read it later,"
+    "You can skip it during the lab session and read it later."
    ]
   },
   {
@@ -69,20 +69,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Machine learning algorithms are programs that learn a model from a dataset with the aim of making predictions or learning structures to organize the data.\n",
+    "Machine learning algorithms are programs that learn a model from a dataset to make predictions or learn structures to organize the data.\n",
     "\n",
-    "In scikit-learn, machine learning algorithms take as an input a *numpy* array (n_samples, n_features), where\n",
+    "In scikit-learn, machine learning algorithms take as input a *numpy* array (n_samples, n_features), where\n",
-    "* **n_samples**: number of samples. Each sample  is an item to process (i.e. classify). A sample can be a document, a picture, a sound, a video, a row in database or CSV file, or whatever you can describe with a fixed set of quantitative traits.\n",
+    "* **n_samples**: number of samples. Each sample  is an item to process (i.e., classify). A sample can be a document, a picture, a sound, a video, a row in a database or CSV file, or whatever you can describe with a fixed set of quantitative traits.\n",
-    "* **n_features**: The number of features or distinct traits that can be used to describe each item in a quantitative manner.\n",
+    "* **n_features**: The number of features or distinct traits that can be used to describe each item quantitatively.\n",
     "\n",
-    "The number of features should be defined in advance. There is a specific type of feature sets that are high dimensional (e.g. millions of features), but most of the values are zero for a given sample. Using (numpy) arrays, all those values that are zero would also take up memory. For this reason, these feature sets are often represented with sparse matrices (scipy.sparse) instead of (numpy) arrays.\n",
+    "The number of features should be defined in advance. A specific type of feature set is high-dimensional (e.g., millions of features), but most values are zero for a given sample. Using (numpy) arrays, all those zero values would also take up memory. For this reason, these feature sets are often represented with sparse matrices (scipy.sparse) instead of (numpy) arrays.\n",
     "\n",
     "The first step in machine learning is **identifying the relevant features** from the input data, and the second step is **extracting the features** from the input data. \n",
     "\n",
     "[Machine learning algorithms](http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/) can be classified according to learning style into:\n",
     "* **Supervised learning**: input data (training dataset) has a known label or result. Example problems are classification and regression. A model is prepared through a training process where it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data.\n",
-    "* **Unsupervised learning**: input data is not labeled. A model is prepared by deducing structures present in the input data. This may be to extract general rules. Example problems are clustering, dimensionality reduction and association rule learning.\n",
+    "* **Unsupervised learning**: input data is not labeled. A model is prepared by deducing structures present in the input data. This may be to extract general rules. Example problems are clustering, dimensionality reduction, and association rule learning.\n",
-    "* **Semi-supervised learning**:i nput data is a mixture of labeled and unlabeled examples. There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions. Example problems are classification and regression."
+    "* **Semi-supervised learning**: input data is a mixture of labeled and unlabeled examples. There is a desired prediction problem, but the model must learn the structures to organize the data and make predictions. Example problems are classification and regression."
    ] 
   },
   {
@@ -96,8 +96,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In *supervised machine learning models*, the machine learning algorithm takes as an input a training dataset, composed of feature vectors and labels, and produces a predictive model which is used for make prediction on new data.\n",
+    "In *supervised machine learning models*, the machine learning algorithm takes as input a training dataset, composed of feature vectors and labels, and produces a predictive model used to predict new data.\n",
-    "![](files/images/plot_ML_flow_chart_1.png)"
+    "![](./images/plot_ML_flow_chart_1.png)"
    ]
   },
   {
@@ -111,7 +111,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In *unsupervised machine learning models*, the machine learning model algorithm takes as an input the feature vectors and produces a predictive model that is used to fit its parameters so as to best summarize regularities found in the data.\n",
+    "In *unsupervised machine learning models*, the machine learning model algorithm takes as input the feature vectors. It produces a predictive model that is used to fit its parameters to summarize the best regularities found in the data.\n",
     "![](files/images/plot_ML_flow_chart_3.png)"
    ]
   },
@@ -129,15 +129,15 @@
     "scikit-learn has a uniform interface for all the estimators, some methods are only available if the estimator is supervised or unsupervised:\n",
     "\n",
     "* Available in *all estimators*:\n",
-    "    * **model.fit()**: fit training data. For supervised learning applications, this accepts two arguments: the data X and the labels y (e.g. model.fit(X, y)). For unsupervised learning applications, this accepts only a single argument, the data X (e.g. model.fit(X)).\n",
+    "    * **model.fit()**: fit training data. For supervised learning applications, this accepts two arguments: the data X and the labels y (e.g., model.fit(X, y)). For unsupervised learning applications, this accepts only a single argument, the data X (e.g. model.fit(X)).\n",
     "\n",
     "* Available in *supervised estimators*:\n",
-    "    * **model.predict()**: given a trained model, predict the label of a new set of data. This method accepts one argument, the new data X_new (e.g. model.predict(X_new)), and returns the learned label for each object in the array.\n",
+    "    * **model.predict()**: given a trained model, predict the label of a new dataset. This method accepts one argument, the new data X_new (e.g., model.predict(X_new)), and returns the learned label for each object in the array.\n",
     "    * **model.predict_proba()**: For classification problems, some estimators also provide this method, which returns the probability that a new observation has each categorical label. In this case, the label with the highest probability is returned by model.predict().\n",
     "\n",
     "* Available in *unsupervised estimators*:\n",
     "    * **model.transform()**: given an unsupervised model, transform new data into the new basis. This also accepts one argument X_new, and returns the new representation of the data based on the unsupervised model.\n",
-    "    * **model.fit_transform()**: some estimators implement this method, which performs a fit and a transform on the same input data.\n",
+    "    * **model.fit_transform()**: Some estimators implement this method, which performs a fit and a transform on the same input data.\n",
     "\n",
     "\n",
     "![](files/images/plot_ML_flow_chart_2.png)"
@@ -154,7 +154,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "* [General concepts of machine learning with scikit-learn](http://www.astroml.org/sklearn_tutorial/general_concepts.html)\n",
+    "* [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/index.html)\n",
     "* [A Tour of Machine Learning Algorithms](http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/)"
    ]
   },
@@ -169,7 +169,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/).  \n",
+    "The notebook is freely licensed under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/).  \n",
     "\n",
     "© Carlos A. Iglesias, Universidad Politécnica de Madrid."
    ]
@@ -177,7 +177,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -191,7 +191,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.6"
+   "version": "3.8.12"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,

ml1/2_5_2_Decision_Tree_Model.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![](files/images/EscUpmPolit_p.gif \"UPM\")"
+    "![](./images/EscUpmPolit_p.gif \"UPM\")"
    ]
   },
   {
@@ -56,9 +56,9 @@
    "source": [
     "The goal of this notebook is to learn how to create a classification object using a [decision tree learning algorithm](https://en.wikipedia.org/wiki/Decision_tree_learning). \n",
     "\n",
-    "There are a number of well known machine learning algorithms for decision tree learning, such as ID3, C4.5, C5.0 and CART. The scikit-learn uses an optimised version of the [CART (Classification and Regression Trees) algorithm](https://en.wikipedia.org/wiki/Predictive_analytics#Classification_and_regression_trees).\n",
+    "There are several well-known machine learning algorithms for decision tree learning, such as ID3, C4.5, C5.0, and CART. The scikit-learn uses an optimised version of the [CART (Classification and Regression Trees) algorithm](https://en.wikipedia.org/wiki/Predictive_analytics#Classification_and_regression_trees).\n",
     "\n",
-    "This notebook will follow the same steps that the previous notebook for learning using the [kNN Model](2_5_1_kNN_Model.ipynb), and details some peculiarities of the decision tree algorithms.\n",
+    "This notebook will follow the same steps as the previous notebook for learning using the [kNN Model](2_5_1_kNN_Model.ipynb), and details some peculiarities of the decision tree algorithms.\n",
     "\n",
     "You need to install pydotplus: `conda install pydotplus` for the visualization."
    ]
@@ -69,7 +69,7 @@
    "source": [
     "## Load data and preprocessing\n",
     "\n",
-    "Here we repeat the same operations for loading data and preprocessing than in the previous notebooks."
+    "Here we repeat the same operations for loading data and preprocessing as in the previous notebooks."
    ]
   },
   {
@@ -130,12 +130,7 @@
     {
      "data": {
       "text/plain": [
-       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=3,\n",
+       "DecisionTreeClassifier(max_depth=3, random_state=1)"
-       "            max_features=None, max_leaf_nodes=None,\n",
-       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
-       "            min_samples_leaf=1, min_samples_split=2,\n",
-       "            min_weight_fraction_leaf=0.0, presort=False, random_state=1,\n",
-       "            splitter='best')"
       ]
      },
      "execution_count": 2,
@@ -267,8 +262,8 @@
     "The current version of pydot does not work well in Python 3.\n",
     "For obtaining an image, you need to install `pip install pydotplus` and then `conda install graphviz`.\n",
     "\n",
-    "You can skip this example. Since it can require installing additional packages, we include here the result.\n",
+    "You can skip this example. Since it can require installing additional packages, we have included the result here.\n",
-    "![Decision Tree](files/images/cart.png)"
+    "![Decision Tree](./images/cart.png)"
    ]
   },
   {
@@ -277,20 +272,23 @@
    "metadata": {},
    "outputs": [
     {
-     "ename": "ModuleNotFoundError",
+     "ename": "InvocationException",
-     "evalue": "No module named 'pydotplus'",
+     "evalue": "GraphViz's executables not found",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[0;31mInvocationException\u001b[0m                       Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-7-1bf5ec7fb043>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mIPython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexternals\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msix\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mStringIO\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpydotplus\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpydot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mdot_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStringIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/tmp/ipykernel_47326/3723147494.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mgraph\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpydot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph_from_dot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdot_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'iris-tree.png'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pydotplus'"
+      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pydotplus/graphviz.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(path, f, prog)\u001b[0m\n\u001b[1;32m   1808\u001b[0m                 \u001b[0;32mlambda\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1809\u001b[0m                 \u001b[0mf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfrmt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1810\u001b[0;31m                 \u001b[0mprog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprog\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprog\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1811\u001b[0m             )\n\u001b[1;32m   1812\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pydotplus/graphviz.py\u001b[0m in \u001b[0;36mwrite\u001b[0;34m(self, path, prog, format)\u001b[0m\n\u001b[1;32m   1916\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1917\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1918\u001b[0;31m                 \u001b[0mfobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1919\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1920\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pydotplus/graphviz.py\u001b[0m in \u001b[0;36mcreate\u001b[0;34m(self, prog, format)\u001b[0m\n\u001b[1;32m   1957\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfind_graphviz\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1958\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprogs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1959\u001b[0;31m                 raise InvocationException(\n\u001b[0m\u001b[1;32m   1960\u001b[0m                     'GraphViz\\'s executables not found')\n\u001b[1;32m   1961\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mInvocationException\u001b[0m: GraphViz's executables not found"
      ]
     }
    ],
    "source": [
     "from IPython.display import Image \n",
-    "from sklearn.externals.six import StringIO\n",
+    "from six import StringIO\n",
     "import pydotplus as pydot\n",
     "\n",
     "dot_data = StringIO()  \n",
@@ -332,7 +330,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Next we are going to export the pseudocode of the the learnt decision tree."
+    "Next, we will export the pseudocode of the learnt decision tree."
    ]
   },
   {
@@ -380,14 +378,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Precision, recall and f-score"
+    "### Precision, recall, and f-score"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For evaluating classification algorithms, we usually calculate three metrics: precision, recall and F1-score\n",
+    "For evaluating classification algorithms, we usually calculate three metrics: precision, recall, and F1-score\n",
     "\n",
     "* **Precision**: This computes the proportion of instances predicted as positives that were correctly evaluated (it measures how right our classifier is when it says that an instance is positive).\n",
     "* **Recall**: This counts the proportion of positive instances that were correctly evaluated (measuring how right our classifier is when faced with a positive instance).\n",
@@ -414,7 +412,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Another useful metric is the confusion matrix"
+    "Another useful metric is the confusion matrix."
    ]
   },
   {
@@ -430,7 +428,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We see we classify well all the 'setosa' and 'versicolor' samples. "
+    "We classify all the 'setosa' and 'versicolor' samples well. "
    ]
   },
   {
@@ -444,7 +442,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In order to avoid bias in the training and testing dataset partition, it is recommended to use **k-fold validation**.\n",
+    "To avoid bias in the training and testing dataset partition, it is recommended to use **k-fold validation**.\n",
     "\n",
     "Sklearn comes with other strategies for [cross validation](http://scikit-learn.org/stable/modules/cross_validation.html#cross-validation), such as stratified K-fold, label k-fold, Leave-One-Out, Leave-P-Out, Leave-One-Label-Out, Leave-P-Label-Out or Shuffle & Split."
    ]
@@ -468,7 +466,7 @@
     "# create a k-fold cross validation iterator of k=10 folds\n",
     "cv = KFold(10, shuffle=True, random_state=33)\n",
     "\n",
-    "# by default the score used is the one returned by score method of the estimator (accuracy)\n",
+    "# by default the score used is the one returned by the score method of the estimator (accuracy)\n",
     "scores = cross_val_score(model, x_iris, y_iris, cv=cv)\n",
     "print(scores)"
    ]
@@ -477,7 +475,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We get an array of k scores. We can calculate the mean and the standard error to obtain a final figure"
+    "We get an array of k scores. We can calculate the mean and the standard error to obtain a final figure."
    ]
   },
   {
@@ -510,10 +508,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "* [Plot the decision surface of a decision tree on the iris dataset](http://scikit-learn.org/stable/auto_examples/tree/plot_iris.html)\n",
+    "* [Plot the decision surface of a decision tree on the iris dataset](https://scikit-learn.org/stable/auto_examples/tree/plot_iris_dtc.html)\n",
-    "* [Learning scikit-learn: Machine Learning in Python](http://proquest.safaribooksonline.com/book/programming/python/9781783281930/1dot-machine-learning-a-gentle-introduction/ch01s02_html), Raúl Garreta; Guillermo Moncecchi, Packt Publishing, 2013.\n",
+    "* [Parameter estimation using grid search with cross-validation](https://scikit-learn.org/stable/auto_examples/model_selection/plot_grid_search_digits.html)\n",
-    "* [Python Machine Learning](http://proquest.safaribooksonline.com/book/programming/python/9781783555130), Sebastian Raschka, Packt Publishing, 2015.\n",
-    "* [Parameter estimation using grid search with cross-validation](http://scikit-learn.org/stable/auto_examples/model_selection/grid_search_digits.html)\n",
     "* [Decision trees in python with scikit-learn and pandas](http://chrisstrelioff.ws/sandbox/2015/06/08/decision_trees_in_python_with_scikit_learn_and_pandas.html)"
    ]
   },
@@ -522,15 +518,24 @@
    "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",
+    "The notebook is freely licensed under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/).  \n",
     "\n",
     "© Carlos A. Iglesias, Universidad Politécnica de Madrid."
    ]
   }
  ],
  "metadata": {
+  "datacleaner": {
+   "position": {
+    "top": "50px"
+   },
+   "python": {
+    "varRefreshCmd": "try:\n    print(_datacleaner.dataframe_metadata())\nexcept:\n    print([])"
+   },
+   "window_display": false
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -544,7 +549,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.8.12"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,

ml1/2_6_Model_Tuning.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![](files/images/EscUpmPolit_p.gif \"UPM\")"
+    "![](./images/EscUpmPolit_p.gif \"UPM\")"
    ]
   },
   {
@@ -39,7 +39,7 @@
     "* [Train classifier](#Train-classifier)\n",
     "* [More about Pipelines](#More-about-Pipelines)\n",
     "* [Tuning the algorithm](#Tuning-the-algorithm)\n",
-    "\t* [Grid Search for Parameter optimization](#Grid-Search-for-Parameter-optimization)\n",
+    "\t* [Grid Search for Hyperparameter optimization](#Grid-Search-for-Hyperparameter-optimization)\n",
     "* [Evaluating the algorithm](#Evaluating-the-algorithm)\n",
     "\t* [K-Fold validation](#K-Fold-validation)\n",
     "* [References](#References)\n"
@@ -56,9 +56,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the previous [notebook](2_5_2_Decision_Tree_Model.ipynb), we got an accuracy of 9.47. Could we get a better accuracy if we tune the parameters of the estimator?\n",
+    "In the previous [notebook](2_5_2_Decision_Tree_Model.ipynb), we got an accuracy of 9.47. Could we get a better accuracy if we tune the hyperparameters of the estimator?\n",
     "\n",
-    "The goal of this notebook is to learn how to tune an algorithm by opimizing its parameters using grid search."
+    "This notebook aims to learn how to tune an algorithm by optimizing its hyperparameters using grid search."
    ]
   },
   {
@@ -137,7 +137,7 @@
     "# create a k-fold cross validation iterator of k=10 folds\n",
     "cv = KFold(10, shuffle=True, random_state=33)\n",
     "\n",
-    "# by default the score used is the one returned by score method of the estimator (accuracy)\n",
+    "# by default the score used is the one returned by the score method of the estimator (accuracy)\n",
     "scores = cross_val_score(model, x_iris, y_iris, cv=cv)\n",
     "\n",
     "from scipy.stats import sem\n",
@@ -189,7 +189,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can get the list of parameters of the model. As you will observe, the parameters of the estimators in the pipeline can be accessed using the <estimator>__<parameter> syntax. We will use this for tuning the parameters."
+    "We can get the list of model parameters. As you will observe, the parameters of the estimators in the pipeline can be accessed using the <estimator>__<parameter> syntax. We will use this for tuning the parameters."
    ]
   },
   {
@@ -205,7 +205,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's see what happens if we change a parameter"
+    "Let's see what happens if we change a parameter."
    ]
   },
   {
@@ -284,7 +284,7 @@
     "\n",
     "Look at the [API](http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html) of *scikit-learn* to understand better the algorithm, as well as which parameters can be tuned. As you see, we can change several ones, such as *criterion*, *splitter*, *max_features*, *max_depth*, *min_samples_split*, *class_weight*, etc.\n",
     "\n",
-    "We can get the full list parameters of an estimator with the method *get_params()*. "
+    "We can get an estimator's full list of parameters with the method *get_params()*. "
    ]
   },
   {
@@ -300,30 +300,30 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can try different values for these parameters and observe the results."
+    "You can try different values for these hyperparameters and observe the results."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Grid Search for Parameter optimization"
+    "### Grid Search for Hyperparameter optimization"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Changing manually the parameters to find their optimal values is not practical. Instead, we can consider to find the optimal value of the parameters as an *optimization problem*. \n",
+    "Changing manually the hyperparameters to find their optimal values is not practical. Instead, we can consider finding the optimal value of the hyperparameters as an *optimization problem*. \n",
     "\n",
-    "The sklearn comes with several optimization techniques for this purpose, such as  **grid search** and  **randomized search**. In this notebook we are going to introduce the former one."
+    "Sklearn has several optimization techniques, such as  **grid search** and  **randomized search**. In this notebook, we are going to introduce the former one."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The sklearn provides an object that, given data, computes the score during the fit of an estimator on a parameter grid and chooses the parameters to maximize the cross-validation score. "
+    "Sklearn provides an object that, given data, computes the score during the fit of an estimator on a hyperparameter grid and chooses the hyperparameters to maximize the cross-validation score. "
    ]
   },
   {
@@ -351,7 +351,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now we are going to show the results of grid search"
+    "Now we are going to show the results of the grid search"
    ]
   },
   {
@@ -371,7 +371,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can now evaluate the KFold with this optimized parameter as follows."
+    "We can now evaluate the KFold with this optimized hyperparameter as follows."
    ]
   },
   {
@@ -392,7 +392,7 @@
     "# create a k-fold cross validation iterator of k=10 folds\n",
     "cv = KFold(10, shuffle=True, random_state=33)\n",
     "\n",
-    "# by default the score used is the one returned by score method of the estimator (accuracy)\n",
+    "# by default the score used is the one returned by the score method of the estimator (accuracy)\n",
     "scores = cross_val_score(model, x_iris, y_iris, cv=cv)\n",
     "def mean_score(scores):\n",
     "    return (\"Mean score: {0:.3f} (+/- {1:.3f})\").format(np.mean(scores), sem(scores))\n",
@@ -405,7 +405,7 @@
    "source": [
     "We have got an *improvement* from 0.947 to 0.953 with k-fold.\n",
     "\n",
-    "We are now to try to fit the best combination of the parameters of the algorithm. It can take some time to compute it."
+    "We are now trying to fit the best combination of the hyperparameters of the algorithm. It can take some time to compute it."
    ]
   },
   {
@@ -414,12 +414,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Set the parameters by cross-validation\n",
+    "# Set the hyperparameters by cross-validation\n",
     "\n",
-    "from sklearn.metrics import classification_report\n",
+    "from sklearn.metrics import classification_report, recall_score, precision_score, make_scorer\n",
     "\n",
-    "# set of parameters to test\n",
+    "# set of hyperparameters to test\n",
-    "tuned_parameters = [{'max_depth': np.arange(3, 10),\n",
+    "tuned_hyperparameters = [{'max_depth': np.arange(3, 10),\n",
     "#                     'max_weights': [1, 10, 100, 1000]},\n",
     "                     'criterion': ['gini', 'entropy'], \n",
     "                     'splitter': ['best', 'random'],\n",
@@ -431,14 +431,19 @@
     "scores = ['precision', 'recall']\n",
     "\n",
     "for score in scores:\n",
-    "    print(\"# Tuning hyper-parameters for %s\" % score)\n",
+    "    print(\"# Tuning hyperparameters for %s\" % score)\n",
     "    print()\n",
     "\n",
+    "    if score == 'precision':\n",
+    "        scorer = make_scorer(precision_score, average='weighted', zero_division=0)\n",
+    "    elif score == 'recall':\n",
+    "        scorer = make_scorer(recall_score, average='weighted', zero_division=0)\n",
+    "    \n",
     "    # cv = the fold of the cross-validation cv, defaulted to 5\n",
-    "    gs = GridSearchCV(DecisionTreeClassifier(), tuned_parameters, cv=10, scoring='%s_weighted' % score)\n",
+    "    gs = GridSearchCV(DecisionTreeClassifier(), tuned_hyperparameters, cv=10, scoring=scorer)\n",
     "    gs.fit(x_train, y_train)\n",
     "\n",
-    "    print(\"Best parameters set found on development set:\")\n",
+    "    print(\"Best hyperparameters set found on development set:\")\n",
     "    print()\n",
     "    print(gs.best_params_)\n",
     "    print()\n",
@@ -487,7 +492,7 @@
     "# create a k-fold cross validation iterator of k=10 folds\n",
     "cv = KFold(10, shuffle=True, random_state=33)\n",
     "\n",
-    "# by default the score used is the one returned by score method of the estimator (accuracy)\n",
+    "# by default the score used is the one returned by the score method of the estimator (accuracy)\n",
     "scores = cross_val_score(model, x_iris, y_iris, cv=cv)\n",
     "def mean_score(scores):\n",
     "    return (\"Mean score: {0:.3f} (+/- {1:.3f})\").format(np.mean(scores), sem(scores))\n",
@@ -512,10 +517,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "* [Plot the decision surface of a decision tree on the iris dataset](http://scikit-learn.org/stable/auto_examples/tree/plot_iris.html)\n",
+    "* [Plot the decision surface of a decision tree on the iris dataset](https://scikit-learn.org/stable/auto_examples/tree/plot_iris_dtc.html)\n",
-    "* [Learning scikit-learn: Machine Learning in Python](http://proquest.safaribooksonline.com/book/programming/python/9781783281930/1dot-machine-learning-a-gentle-introduction/ch01s02_html), Raúl Garreta; Guillermo Moncecchi, Packt Publishing, 2013.\n",
+    "* [Hyperparameter estimation using grid search with cross-validation](http://scikit-learn.org/stable/auto_examples/model_selection/grid_search_digits.html)\n",
-    "* [Python Machine Learning](http://proquest.safaribooksonline.com/book/programming/python/9781783555130), Sebastian Raschka, Packt Publishing, 2015.\n",
-    "* [Parameter estimation using grid search with cross-validation](http://scikit-learn.org/stable/auto_examples/model_selection/grid_search_digits.html)\n",
     "* [Decision trees in python with scikit-learn and pandas](http://chrisstrelioff.ws/sandbox/2015/06/08/decision_trees_in_python_with_scikit_learn_and_pandas.html)"
    ]
   },
@@ -530,7 +533,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/).  \n",
+    "The notebook is freely licensed under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/).  \n",
     "\n",
     "©  Carlos A. Iglesias, Universidad Politécnica de Madrid."
    ]
@@ -538,7 +541,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -552,7 +555,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.7"
+   "version": "3.8.12"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,

ml1/2_7_Model_Persistence.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![](files/images/EscUpmPolit_p.gif \"UPM\")"
+    "![](./images/EscUpmPolit_p.gif \"UPM\")"
    ]
   },
   {
@@ -48,9 +48,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The goal of this notebook is to learn how to save a model in the the scikit by using Python’s built-in persistence model, namely pickle\n",
+    "The goal of this notebook is to learn how to save a model in the scikit by using Python’s built-in persistence model, namely pickle\n",
     "\n",
-    "First we recap the previous tasks: load data, preprocess and train the model."
+    "First, we recap the previous tasks: load data, preprocess, and train the model."
    ]
   },
   {
@@ -107,7 +107,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A more efficient alternative to pickle is joblib, especially for big data problems. In this case the model can only be saved to a file and not to a string."
+    "A more efficient alternative to pickle is joblib, especially for big data problems. In this case, the model can only be saved to a file and not to a string."
    ]
   },
   {
@@ -117,7 +117,7 @@
    "outputs": [],
    "source": [
     "# save model\n",
-    "from sklearn.externals import joblib\n",
+    "import joblib\n",
     "joblib.dump(model, 'filename.pkl') \n",
     "\n",
     "#load model\n",
@@ -136,7 +136,9 @@
    "metadata": {},
    "source": [
     "* [Tutorial scikit-learn](http://scikit-learn.org/stable/tutorial/basic/tutorial.html)\n",
-    "* [Model persistence in scikit-learn](http://scikit-learn.org/stable/modules/model_persistence.html#model-persistence)"
+    "* [Model persistence in scikit-learn](http://scikit-learn.org/stable/modules/model_persistence.html#model-persistence)\n",
+    "* [scikit-learn : Machine Learning Simplified](https://learning.oreilly.com/library/view/scikit-learn-machine/9781788833479/), Raúl Garreta; Guillermo Moncecchi, Packt Publishing, 2017.\n",
+    "* [Python Machine Learning](https://learning.oreilly.com/library/view/python-machine-learning/9781789955750/), Sebastian Raschka, Packt Publishing, 2019."
    ]
   },
   {
@@ -144,15 +146,24 @@
    "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",
+    "The notebook is freely licensed under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/).  \n",
     "\n",
     "©  Carlos A. Iglesias, Universidad Politécnica de Madrid."
    ]
   }
  ],
  "metadata": {
+  "datacleaner": {
+   "position": {
+    "top": "50px"
+   },
+   "python": {
+    "varRefreshCmd": "try:\n    print(_datacleaner.dataframe_metadata())\nexcept:\n    print([])"
+   },
+   "window_display": false
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -166,7 +177,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.7"
+   "version": "3.8.12"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,

ml1/2_8_Conclusions.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![](files/images/EscUpmPolit_p.gif \"UPM\")"
+    "![](./images/EscUpmPolit_p.gif \"UPM\")"
    ]
   },
   {
@@ -52,7 +52,7 @@
     "\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",
+    "The plots show training points in solid colors and testing points in semi-transparent colors. 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",
@@ -94,7 +94,7 @@
    "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",
+    "The notebook is freely licensed under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/).  \n",
     "\n",
     "©  Carlos A. Iglesias, Universidad Politécnica de Madrid."
    ]

ml1/util_ds.py

@@ -47,7 +47,7 @@ def get_code(tree, feature_names, target_names,
 
     recurse(left, right, threshold, features, 0, 0)
 
-# Taken from http://scikit-learn.org/stable/auto_examples/tree/plot_iris.html#example-tree-plot-iris-py
+# Taken from https://scikit-learn.org/stable/auto_examples/tree/plot_iris_dtc.html
 import numpy as np
 import matplotlib.pyplot as plt
 

ml1/util_knn.py

@@ -2,6 +2,7 @@ import numpy as np
 import matplotlib.pyplot as plt
 from matplotlib.colors import ListedColormap
 from sklearn import neighbors, datasets
+import seaborn as sns
 from sklearn.neighbors import KNeighborsClassifier
 
 # Taken from http://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html
@@ -19,9 +20,9 @@ def plot_classification_iris():
     h = .02  # step size in the mesh
     n_neighbors = 15
 
-    # Create color maps
+  # Create color maps
-    cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
+    cmap_light = ListedColormap(['orange', 'cyan', 'cornflowerblue'])
-    cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])
+    cmap_bold = ['darkorange', 'c', 'darkblue']
 
     for weights in ['uniform', 'distance']:
         # we create an instance of Neighbours Classifier and fit the data.
@@ -29,7 +30,7 @@ def plot_classification_iris():
         clf.fit(X, y)
 
         # Plot the decision boundary. For that, we will assign a color to each
-        # point in the mesh [x_min, m_max]x[y_min, y_max].
+        # point in the mesh [x_min, x_max]x[y_min, y_max].
         x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
         y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
         xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
@@ -38,14 +39,17 @@ def plot_classification_iris():
 
         # Put the result into a color plot
         Z = Z.reshape(xx.shape)
-        plt.figure()
+        plt.figure(figsize=(8, 6))
-        plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
+        plt.contourf(xx, yy, Z, cmap=cmap_light)
 
         # Plot also the training points
-        plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)
+        sns.scatterplot(x=X[:, 0], y=X[:, 1], hue=iris.target_names[y],
+                        palette=cmap_bold, alpha=1.0, edgecolor="black")
         plt.xlim(xx.min(), xx.max())
         plt.ylim(yy.min(), yy.max())
         plt.title("3-Class classification (k = %i, weights = '%s')"
-                % (n_neighbors, weights))
+                  % (n_neighbors, weights))
+        plt.xlabel(iris.feature_names[0])
+        plt.ylabel(iris.feature_names[1])
 
-    plt.show()
+plt.show()

ml2/3_0_0_Intro_ML_2.ipynb

@@ -74,9 +74,7 @@
    "metadata": {},
    "source": [
     "* [IPython Notebook Tutorial for Titanic: Machine Learning from Disaster](https://www.kaggle.com/c/titanic/forums/t/5105/ipython-notebook-tutorial-for-titanic-machine-learning-from-disaster)\n",
-    "* [Scikit-learn videos](http://blog.kaggle.com/author/kevin-markham/) and [notebooks](https://github.com/justmarkham/scikit-learn-videos) by Kevin Marham\n",
+    "* [Scikit-learn videos and notebooks](https://github.com/justmarkham/scikit-learn-videos) by Kevin Marham\n"
-    "* [Learning scikit-learn: Machine Learning in Python](http://proquest.safaribooksonline.com/book/programming/python/9781783281930/1dot-machine-learning-a-gentle-introduction/ch01s02_html), Raúl Garreta; Guillermo Moncecchi, Packt Publishing, 2013.\n",
-    "* [Python Machine Learning](http://proquest.safaribooksonline.com/book/programming/python/9781783555130), Sebastian Raschka, Packt Publishing, 2015."
    ]
   },
   {
@@ -92,7 +90,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -106,7 +104,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.8.12"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,

ml2/3_1_Read_Data.ipynb

@@ -50,30 +50,30 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this session we will work with the Titanic dataset. This dataset is provided by [Kaggle](http://www.kaggle.com). Kaggle is a crowdsourcing platform that organizes competitions where researchers and companies post their data and users compete to obtain the best models.\n",
+    "In this session, we will work with the Titanic dataset. This dataset is provided by [Kaggle](http://www.kaggle.com). Kaggle is a crowdsourcing platform that organizes competitions where researchers and companies post their data and users compete to obtain the best models.\n",
     "\n",
     "![Titanic](images/titanic.jpg)\n",
     "\n",
     "\n",
-    "The main objective is predicting which passengers survived the sinking of the Titanic.\n",
+    "The main objective is to predict which passengers survived the sinking of the Titanic.\n",
     "\n",
     "The data is available [here](https://www.kaggle.com/c/titanic/data). There are two files, one for training ([train.csv](files/data-titanic/train.csv)) and another file for testing [test.csv](files/data-titanic/test.csv). A local copy has been included in this notebook under the folder *data-titanic*.\n",
     "\n",
     "\n",
     "Here follows a description of the variables.\n",
     "\n",
-    "|Variable | Description| Values|\n",
+    "|  Variable  |          Description            |       Values    |\n",
-    "|-------------------------------|\n",
+    "|------------|---------------------------------|-----------------|\n",
-    "| survival| Survival| (0 = No; 1 = Yes)|\n",
+    "|  survival  |           Survival              |(0 = No; 1 = Yes)|\n",
-    "|Pclass |Name | |\n",
+    "|    Pclass  |             Name                |                 |\n",
-    "|Sex  |Sex | male, female|\n",
+    "|     Sex    |              Sex                |   male, female  |\n",
-    "|Age |Age|\n",
+    "|     Age    |              Age                |                 |\n",
-    "|SibSp |Number of Siblings/Spouses Aboard||\n",
+    "|    SibSp   |Number of Siblings/Spouses Aboard|                 |\n",
-    "|Parch |Number of Parents/Children Aboard||\n",
+    "|     Parch  |Number of Parents/Children Aboard|                 |\n",
-    "|Ticket|Ticket Number||\n",
+    "|     Ticket |        Ticket Number            |                 |\n",
-    "|Fare            |Passenger Fare||\n",
+    "|     Fare   |       Passenger Fare            |                 |\n",
-    "|Cabin           |Cabin||\n",
+    "|     Cabin  |            Cabin                |                 |\n",
-    "|Embarked        |Port of Embarkation| (C = Cherbourg; Q = Queenstown; S = Southampton)|\n",
+    "|   Embarked |       Port of Embarkation       | (C = Cherbourg; Q = Queenstown; S = Southampton)|\n",
     "\n",
     "\n",
     "The definitions used for SibSp and Parch are:\n",
@@ -213,8 +213,7 @@
     "* [Pandas API input-output](http://pandas.pydata.org/pandas-docs/stable/api.html#input-output)\n",
     "* [Pandas API - pandas.read_csv](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html)\n",
     "* [DataFrame](http://pandas.pydata.org/pandas-docs/stable/dsintro.html)\n",
-    "* [An introduction to NumPy and Scipy](http://www.engr.ucsb.edu/~shell/che210d/numpy.pdf)\n",
+    "* [An introduction to NumPy and Scipy](https://sites.engineering.ucsb.edu/~shell/che210d/numpy.pdf)\n"
-    "* [NumPy tutorial](https://docs.scipy.org/doc/numpy-dev/user/quickstart.html)"
    ]
   },
   {

ml2/3_2_Pandas.ipynb

@@ -433,10 +433,9 @@
    "metadata": {},
    "source": [
     "* [Pandas](http://pandas.pydata.org/)\n",
-    "* [Learning Pandas, Michael Heydt, Packt Publishing, 2015](http://proquest.safaribooksonline.com/book/programming/python/9781783985128)\n",
+    "* [Pandas. Introduction to Data Structures](https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html)\n",
-    "* [Pandas. Introduction to Data Structures](http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dsintro)\n",
     "* [Introducing Pandas Objects](https://www.oreilly.com/learning/introducing-pandas-objects)\n",
-    "* [Boolean Operators in Pandas](http://pandas.pydata.org/pandas-docs/stable/indexing.html#boolean-operators)"
+    "* [Boolean Operators in Pandas](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#boolean-operators)"
    ]
   },
   {
@@ -458,7 +457,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -472,7 +471,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.8.12"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,

ml2/3_3_Data_Munging_with_Pandas.ipynb

@@ -373,8 +373,8 @@
    "source": [
     "#Mean age of  passengers per Passenger class\n",
     "\n",
-    "#First we calculate the mean\n",
+    "#First we calculate the mean for the numeric columns\n",
-    "df.groupby('Pclass').mean()"
+    "df.select_dtypes(np.number).groupby('Pclass').mean()"
    ]
   },
   {
@@ -404,7 +404,7 @@
    "outputs": [],
    "source": [
     "#Mean Age and SibSp of passengers grouped by passenger class and sex\n",
-    "df.groupby(['Pclass', 'Sex'])['Age','SibSp'].mean()"
+    "df.groupby(['Pclass', 'Sex'])[['Age','SibSp']].mean()"
    ]
   },
   {
@@ -414,7 +414,7 @@
    "outputs": [],
    "source": [
     "#Show mean  Age and  SibSp for passengers  older than 25 grouped by Passenger Class and Sex\n",
-    "df[df.Age > 25].groupby(['Pclass', 'Sex'])['Age','SibSp'].mean()"
+    "df[df.Age > 25].groupby(['Pclass', 'Sex'])[['Age','SibSp']].mean()"
    ]
   },
   {
@@ -424,7 +424,7 @@
    "outputs": [],
    "source": [
     "# Mean age, SibSp , Survived of passengers older than 25 which survived, grouped by Passenger Class and Sex \n",
-    "df[(df.Age > 25 & (df.Survived == 1))].groupby(['Pclass', 'Sex'])['Age','SibSp','Survived'].mean()"
+    "df[(df.Age > 25 & (df.Survived == 1))].groupby(['Pclass', 'Sex'])[['Age','SibSp','Survived']].mean()"
    ]
   },
   {
@@ -436,7 +436,7 @@
     "# We can also decide which function apply in each column\n",
     "\n",
     "#Show mean Age, mean SibSp, and number of passengers older than 25 that survived,  grouped by Passenger Class and Sex\n",
-    "df[(df.Age > 25 & (df.Survived == 1))].groupby(['Pclass', 'Sex'])['Age','SibSp','Survived'].agg({'Age': np.mean, \n",
+    "df[(df.Age > 25 & (df.Survived == 1))].groupby(['Pclass', 'Sex'])[['Age','SibSp','Survived']].agg({'Age': np.mean, \n",
     "                                                                         'SibSp': np.mean, 'Survived': np.sum})"
    ]
   },
@@ -451,7 +451,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Pivot tables are an intuitive way to analyze data, and alternative to group columns."
+    "Pivot tables are an intuitive way to analyze data, and an alternative to group columns.\n",
+    "\n",
+    "This command makes a table with rows Sex and columns Pclass, and\n",
+    "averages the result of the column Survived, thereby giving the percentage of survivors in each grouping."
    ]
   },
   {
@@ -460,7 +463,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pd.pivot_table(df, index='Sex')"
+    "pd.pivot_table(df, index='Sex', columns='Pclass', values=['Survived'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we want to analyze multi-index, the percentage of survivoers, given sex and age, and distributed by Pclass."
    ]
   },
   {
@@ -469,7 +479,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pd.pivot_table(df, index=['Sex', 'Pclass'])"
+    "pd.pivot_table(df, index=['Sex', 'Age'], columns=['Pclass'], values=['Survived'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nevertheless, this is not very useful since we have a row per age. Thus, we define a partition."
    ]
   },
   {
@@ -478,7 +495,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pd.pivot_table(df, index=['Sex', 'Pclass'], values=['Age', 'SibSp'])"
+    "# Partition each of the passengers into 3 categories based on their age\n",
+    "age = pd.cut(df['Age'], [0,12,18,80])"
    ]
   },
   {
@@ -487,7 +505,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pd.pivot_table(df, index=['Sex', 'Pclass'], values=['Age', 'SibSp'], aggfunc=np.mean)"
+    "pd.pivot_table(df, index=['Sex', age], columns=['Pclass'], values=['Survived'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can change the function used for aggregating each group."
    ]
   },
   {
@@ -496,8 +521,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Try np.sum, np.size, len\n",
+    "# default\n",
-    "pd.pivot_table(df, index=['Sex', 'Pclass'], values=['Age', 'SibSp'], aggfunc=[np.mean, np.sum])"
+    "pd.pivot_table(df, index=['Sex', age], columns=['Pclass'], values=['Survived'], aggfunc=np.mean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Two agg functions\n",
+    "pd.pivot_table(df, index=['Sex', age], columns=['Pclass'], values=['Survived'], aggfunc=[np.mean, np.sum])"
    ]
   },
   {
@@ -600,8 +635,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Fill missing values with the median\n",
+    "# Fill missing values with the median, we avoid empty (None) values with numeric_only\n",
-    "df_filled = df.fillna(df.median())\n",
+    "df_filled = df.fillna(df.median(numeric_only=True))\n",
     "df_filled[-5:]"
    ]
   },
@@ -685,7 +720,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# But we are working on a copy \n",
+    "# But we are working on a copy, so we get a warning\n",
     "df.iloc[889]['Sex'] = np.nan"
    ]
   },
@@ -695,7 +730,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# If we want to change, we should not chain selections\n",
+    "# If we want to change it, we should not chain selections\n",
     "# The selection can be done with the column name\n",
     "df.loc[889, 'Sex']"
    ]
@@ -932,11 +967,11 @@
    "metadata": {},
    "source": [
     "* [Pandas](http://pandas.pydata.org/)\n",
-    "* [Learning Pandas, Michael Heydt, Packt Publishing, 2015](http://proquest.safaribooksonline.com/book/programming/python/9781783985128)\n",
+    "* [Learning Pandas, Michael Heydt, Packt Publishing, 2017](https://learning.oreilly.com/library/view/learning-pandas/9781787123137/)\n",
-    "* [Useful Pandas Snippets](https://gist.github.com/bsweger/e5817488d161f37dcbd2)\n",
+    "* [Pandas. Introduction to Data Structures](https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html)\n",
-    "* [Pandas. Introduction to Data Structures](http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dsintro)\n",
     "* [Introducing Pandas Objects](https://www.oreilly.com/learning/introducing-pandas-objects)\n",
-    "* [Boolean Operators in Pandas](http://pandas.pydata.org/pandas-docs/stable/indexing.html#boolean-operators)"
+    "* [Boolean Operators in Pandas](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#boolean-operators)\n",
+    "* [Useful Pandas Snippets](https://gist.github.com/bsweger/e5817488d161f37dcbd2)"
    ]
   },
   {
@@ -958,7 +993,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -972,7 +1007,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.11.5"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,

ml2/3_4_Visualisation_Pandas.ipynb

@@ -220,7 +220,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Analise distributon\n",
+    "# Analise distribution\n",
     "df.hist(figsize=(10,10))\n",
     "plt.show()"
    ]
@@ -233,7 +233,7 @@
    "source": [
     "# We can see the pairwise correlation between variables. A value near 0 means low correlation\n",
     "# while a value  near -1 or 1 indicates strong correlation.\n",
-    "df.corr()"
+    "df.corr(numeric_only = True)"
    ]
   },
   {
@@ -249,11 +249,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# General description of relationship betweek variables uwing Seaborn PairGrid\n",
+    "# General description of relationship between variables uwing Seaborn PairGrid\n",
     "# We use df_clean, since the null values of df would gives us an error, you can check it.\n",
     "g = sns.PairGrid(df_clean, hue=\"Survived\")\n",
-    "g.map_diag(plt.hist)\n",
+    "g.map(sns.scatterplot)\n",
-    "g.map_offdiag(plt.scatter)\n",
     "g.add_legend()"
    ]
   },
@@ -367,7 +366,7 @@
    "outputs": [],
    "source": [
     "# Now we visualise age and survived to see if there is some relationship\n",
-    "sns.FacetGrid(df, hue=\"Survived\", size=5).map(sns.kdeplot, \"Age\").add_legend()"
+    "sns.FacetGrid(df, hue=\"Survived\", height=5).map(sns.kdeplot, \"Age\").add_legend()"
    ]
   },
   {
@@ -567,7 +566,7 @@
    "outputs": [],
    "source": [
     "# Plot with seaborn\n",
-    "sns.countplot('Sex', data=df)"
+    "sns.countplot(x='Sex', data=df)"
    ]
   },
   {
@@ -683,16 +682,6 @@
     "df.groupby('Pclass').size()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Distribution\n",
-    "sns.countplot('Pclass', data=df)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -725,7 +714,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sns.factorplot('Pclass',data=df,hue='Sex',kind='count')"
+    "sns.catplot(x='Pclass',data=df,hue='Sex',kind='count')"
    ]
   },
   {
@@ -906,7 +895,7 @@
    "outputs": [],
    "source": [
     "# Distribution\n",
-    "sns.countplot('Embarked', data=df)"
+    "sns.countplot(x='Embarked', data=df)"
    ]
   },
   {
@@ -997,7 +986,7 @@
    "outputs": [],
    "source": [
     "# Distribution\n",
-    "sns.countplot('SibSp', data=df)"
+    "sns.countplot(x='SibSp', data=df)"
    ]
   },
   {
@@ -1180,7 +1169,7 @@
    "outputs": [],
    "source": [
     "# Distribution\n",
-    "sns.countplot('Parch', data=df)"
+    "sns.countplot(x='Parch', data=df)"
    ]
   },
   {
@@ -1233,7 +1222,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df.groupby(['Pclass', 'Sex', 'Parch'])['Parch', 'SibSp', 'Survived'].agg({'Parch': np.size, 'SibSp': np.mean, 'Survived': np.mean})"
+    "df.groupby(['Pclass', 'Sex', 'Parch'])[['Parch', 'SibSp', 'Survived']].agg({'Parch': np.size, 'SibSp': np.mean, 'Survived': np.mean})"
    ]
   },
   {
@@ -1576,7 +1565,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1590,7 +1579,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.8.12"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,

ml2/3_5_Exercise_1.ipynb

@@ -72,7 +72,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Assign the variable *df* a Dataframe with the Titanic Dataset from the URL https://raw.githubusercontent.com/gsi-upm/sitc/master/ml2/data-titanic/train.csv\"\n",
+    "Assign the variable *df* a Dataframe with the Titanic Dataset from the URL https://raw.githubusercontent.com/gsi-upm/sitc/master/ml2/data-titanic/train.csv.\n",
     "\n",
     "Print *df*."
    ]
@@ -214,7 +214,7 @@
    "outputs": [],
    "source": [
     "df['FamilySize'] = df['SibSp'] + df['Parch']\n",
-    "df.head()"
+    "df"
    ]
   },
   {
@@ -377,8 +377,8 @@
    "outputs": [],
    "source": [
     "# Group ages to simplify machine learning algorithms.  0: 0-5, 1: 6-10, 2: 11-15, 3: 16-59 and 4: 60-80\n",
-    "df['AgeGroup'] = 0\n",
+    "df['AgeGroup'] = np.nan\n",
-    "df.loc[(.Age<6),'AgeGroup'] = 0\n",
+    "df.loc[(df.Age<6),'AgeGroup'] = 0\n",
     "df.loc[(df.Age>=6) & (df.Age < 11),'AgeGroup'] = 1\n",
     "df.loc[(df.Age>=11) & (df.Age < 16),'AgeGroup'] = 2\n",
     "df.loc[(df.Age>=16) & (df.Age < 60),'AgeGroup'] = 3\n",
@@ -404,8 +404,8 @@
     "        if np.isnan(big_string):\n",
     "            return 'X'\n",
     "    for substring in substrings:\n",
-    "        if big_string.find(substring) != 1:\n",
+    "        if substring in big_string:\n",
-    "            return substring\n",
+    "            return substring[0::]\n",
     "    print(big_string)\n",
     "    return 'X'\n",
     " \n",
@@ -478,8 +478,17 @@
   }
  ],
  "metadata": {
+  "datacleaner": {
+   "position": {
+    "top": "50px"
+   },
+   "python": {
+    "varRefreshCmd": "try:\n    print(_datacleaner.dataframe_metadata())\nexcept:\n    print([])"
+   },
+   "window_display": false
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -493,7 +502,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.8.12"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,

ml2/3_6_Machine_Learning.ipynb

@@ -78,7 +78,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "* [Python Machine Learning](http://proquest.safaribooksonline.com/book/programming/python/9781783555130), Sebastian Raschka, Packt Publishing, 2015."
+    "* [Python Machine Learning](https://learning.oreilly.com/library/view/python-machine-learning/9781789955750/), Sebastian Raschka and Vahid Mirjalili, Packt Publishing, 2019."
    ]
   },
   {
@@ -100,7 +100,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -114,7 +114,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.8.12"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,

ml2/3_7_SVM.ipynb

@@ -222,7 +222,7 @@
     "kernel = types_of_kernels[0]\n",
     "gamma = 3.0\n",
     "\n",
-    "# Create kNN model\n",
+    "# Create SVM model\n",
     "model = SVC(kernel=kernel, probability=True, gamma=gamma)"
    ]
   },
@@ -276,7 +276,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can evaluate the accuracy if the model always predict the most frequent class, following this [refeference](http://blog.kaggle.com/2015/10/23/scikit-learn-video-9-better-evaluation-of-classification-models/)."
+    "We can evaluate the accuracy if the model always predict the most frequent class, following this [reference](https://medium.com/analytics-vidhya/model-validation-for-classification-5ff4a0373090)."
    ]
   },
   {
@@ -351,10 +351,10 @@
     "We can obtain more information from the confussion matrix and the metric F1-score.\n",
     "In a confussion matrix, we can see:\n",
     "\n",
-    "||**Predicted**: 0| **Predicted: 1**|\n",
+    "|             |**Predicted**: 0| **Predicted: 1**|\n",
-    "|---------------------------|\n",
+    "|-------------|----------------|-----------------|\n",
-    "|**Actual: 0**| TN | FP |\n",
+    "|**Actual: 0**| TN             | FP              |\n",
-    "|**Actual: 1**| FN|TP|\n",
+    "|**Actual: 1**| FN             | TP              |\n",
     "\n",
     "* **True negatives (TN)**: actual negatives that were predicted as negatives\n",
     "* **False positives (FP)**: actual negatives that were predicted as positives\n",
@@ -418,7 +418,7 @@
     "plt.ylim([0.0, 1.0])\n",
     "plt.title('ROC curve for Titanic')\n",
     "plt.xlabel('False Positive Rate (1 - Recall)')\n",
-    "plt.xlabel('True Positive Rate (Sensitivity)')\n",
+    "plt.ylabel('True Positive Rate (Sensitivity)')\n",
     "plt.grid(True)"
    ]
   },
@@ -535,13 +535,13 @@
    "source": [
     "# This step will take some time\n",
     "# Cross-validationt\n",
-    "cv = KFold(n_splits=5, shuffle=False, random_state=33)\n",
+    "cv = KFold(n_splits=5, shuffle=True, random_state=33)\n",
     "# StratifiedKFold has is a variation of k-fold which returns stratified folds:\n",
     "# each set contains approximately the same percentage of samples of each target class as the complete set.\n",
-    "#cv = StratifiedKFold(y, n_folds=3, shuffle=False, random_state=33)\n",
+    "#cv = StratifiedKFold(y, n_folds=3, shuffle=True, random_state=33)\n",
     "scores = cross_val_score(model, X, y, cv=cv)\n",
     "print(\"Scores in every iteration\", scores)\n",
-    "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n"
+    "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))"
    ]
   },
   {
@@ -644,7 +644,7 @@
    "source": [
     "* [Titanic Machine Learning from Disaster](https://www.kaggle.com/c/titanic/forums/t/5105/ipython-notebook-tutorial-for-titanic-machine-learning-from-disaster)\n",
     "* [API SVC scikit-learn](http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html)\n",
-    "* [Better evaluation of classification models](http://blog.kaggle.com/2015/10/23/scikit-learn-video-9-better-evaluation-of-classification-models/)"
+    "* [How to choose the right metric for evaluating an ML model](https://www.kaggle.com/vipulgandhi/how-to-choose-right-metric-for-evaluating-ml-model)"
    ]
   },
   {
@@ -666,7 +666,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -680,7 +680,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.8.12"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,