ml4/2_5_1_Exercise.ipynb

@@ -48,7 +48,7 @@
     "# Introduction\n",
     "The purpose of this practice is to understand better how GAs work. \n",
     "\n",
-    "There are many libraries that implement GAs; you can find some of them in the [References](#References) section."
+    "There are many libraries that implement GAs, you can find some of then in the [References](#References) section."
    ]
   },
   {
@@ -56,7 +56,7 @@
    "metadata": {},
    "source": [
     "# Genetic Algorithms\n",
-    "In this section, we are going to use the library [DEAP](https://github.com/DEAP/deap/tree/master) for implementing a genetic algorithms.\n",
+    "In this section we are going to use the library DEAP [[References](#References)] for implementing a genetic algorithms.\n",
     "\n",
     "We are going to implement the OneMax problem as seen in class.\n",
     "\n",
@@ -200,13 +200,22 @@
    "source": [
     "## Optional. Optimizing ML hyperparameters\n",
     "\n",
-    "One of the applications of Genetic Algorithms is the optimization of ML hyperparameters. Previously, we have used GridSearch from Scikit. Using [sklearn-deap](https://github.com/rsteca/sklearn-deap), optimize the Titatic hyperparameters using both GridSearch and Genetic Algorithms. \n",
+    "One of the applications of Genetic Algorithms is the optimization of ML hyperparameters. Previously we have used GridSearch from Scikit. Using (sklearn-deap)[[References](#References)], optimize the Titatic hyperparameters using both GridSearch and Genetic Algorithms. \n",
     "\n",
     "The same exercise (using the digits dataset) can be found in this [notebook](https://github.com/rsteca/sklearn-deap/blob/master/test.ipynb).\n",
     "\n",
-    "Since there is a problem with Scikit version 0.24, you can just comment on the different approaches.",
+    "Submit a notebook where you include well-crafted conclusions about the exercises, discussing the pros and cons of using genetic algorithms for this purpose.\n",
     "\n",
-    "Alternatively, you can also use  the library [sklearn-genetic-opt](https://sklearn-genetic-opt.readthedocs.io/en/stable/index.html) and discuss the digit classification example included in the library: [digits decision tree](https://sklearn-genetic-opt.readthedocs.io/en/stable/notebooks/Digits_decision_tree.html)."
+    "Note: There is a problem with Scikit version 0.24. Comment on the different approaches."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Optional exercises\n",
+    "\n",
+    "Here there is a proposed optional exercise."
    ]
   },
   {
@@ -215,7 +224,7 @@
    "source": [
     "## Optional. Optimizing an ML pipeline with a genetic algorithm\n",
     "\n",
-    "The library [TPOT](https://epistasislab.github.io/tpot/latest/) optimizes ML pipelines and comes with a lot of [examples](https://epistasislab.github.io/tpot/latest/Tutorial/9_Genetic_Algorithm_Overview/) and even notebooks, for example for the [iris dataset](https://github.com/EpistasisLab/tpot/blob/master/tutorials/IRIS.ipynb).\n",
+    "The library [TPOT](#References) optimizes ML pipelines and comes with a lot of (examples)[https://epistasislab.github.io/tpot/examples/] and even notebooks, for example for the [iris dataset](https://github.com/EpistasisLab/tpot/blob/master/tutorials/IRIS.ipynb).\n",
     "\n",
     "Your task is to apply TPOT to the intermediate challenge and write a short essay explaining:\n",
     "* what TPOT does (with your own words).\n",
@@ -233,8 +242,7 @@
     "* [tpot](http://epistasislab.github.io/tpot/)\n",
     "* [gplearn](http://gplearn.readthedocs.io/en/latest/index.html)\n",
     "* [scikit-allel](https://scikit-allel.readthedocs.io/en/latest/)\n",
-    "* [sklearn-genetic](https://github.com/manuel-calzolari/sklearn-genetic)\n",
-    "* [sklearn-genetic-opt](https://sklearn-genetic-opt.readthedocs.io/en/stable/)"
+    "* [scklearn-genetic](https://github.com/manuel-calzolari/sklearn-genetic)"
    ]
   },
   {
@@ -248,7 +256,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The notebook is freely licensed under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/).  \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."
    ]