From d80e898ae9b5b82261042d1ebbaa656df1ad83e0 Mon Sep 17 00:00:00 2001 From: mhjensen Date: Fri, 7 Jun 2019 14:16:18 -0400 Subject: [PATCH] readme update --- README.md | 7 +- .../DecisionTrees/ipynb/DecisionTrees.ipynb | 141 +++++++++--------- 2 files changed, 77 insertions(+), 71 deletions(-) diff --git a/README.md b/README.md index 6d606bff3..b452b5def 100644 --- a/README.md +++ b/README.md @@ -54,21 +54,22 @@ The following topics will be covered - Basic concepts, expectation values, variance, covariance, correlation functions and errors; - Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions; - Central elements of Bayesian statistics and modeling; -- Central elements from linear algebra - Gradient methods for data optimization - Monte Carlo methods, Markov chains, Metropolis-Hastings algorithm; - Linear methods for regression and classification; - Estimation of errors using cross-validation, blocking, bootstrapping and jackknife methods; - Practical optimization using Singular-value decomposition and least squares for parameterizing data. - -### Machine learning, mainly supervised learning +### Machine learning The following topics will be covered - Linear Regression and Logistic Regression; - Neural networks and deep learning; - Decisions trees and nearest neighbor algorithms - Support vector machines +- Bayesian Neural Networks +- Boltzmann Machines +- Dimensionality reduction, from PCA to cluster models All the above topics will be supported by examples, hands-on exercises and project work. diff --git a/doc/pub/DecisionTrees/ipynb/DecisionTrees.ipynb b/doc/pub/DecisionTrees/ipynb/DecisionTrees.ipynb index ce51c0b63..34e8a668e 100644 --- a/doc/pub/DecisionTrees/ipynb/DecisionTrees.ipynb +++ b/doc/pub/DecisionTrees/ipynb/DecisionTrees.ipynb @@ -73,10 +73,39 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2nd degree coefficients:\n", + "zero power: -5.3396743101437965\n", + "first power: 0.12395206655103709\n", + "second power: -0.0003817214992490569\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "%matplotlib inline\n", "\n", @@ -180,9 +209,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Program to test the Metropolis algorithm with one particle at given temp in\n", @@ -592,9 +619,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def entropy(target_col):\n", @@ -692,9 +717,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from __future__ import division, print_function, unicode_literals\n", @@ -773,9 +796,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "np.random.seed(6)\n", @@ -810,9 +831,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Quadratic training set + noise\n", @@ -826,9 +845,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.tree import DecisionTreeRegressor\n", @@ -847,9 +864,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.tree import DecisionTreeRegressor\n", @@ -895,9 +910,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "tree_reg1 = DecisionTreeRegressor(random_state=42)\n", @@ -940,9 +953,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", @@ -1048,9 +1059,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "heads_proba = 0.51\n", @@ -1123,9 +1132,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", @@ -1150,9 +1157,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", @@ -1178,9 +1183,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import accuracy_score\n", @@ -1194,9 +1197,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "log_clf = LogisticRegression(random_state=42)\n", @@ -1212,9 +1213,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import accuracy_score\n", @@ -1235,9 +1234,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.ensemble import BaggingClassifier\n", @@ -1253,9 +1250,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import accuracy_score\n", @@ -1265,9 +1260,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "tree_clf = DecisionTreeClassifier(random_state=42)\n", @@ -1279,9 +1272,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from matplotlib.colors import ListedColormap\n", @@ -1322,9 +1313,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "bag_clf = BaggingClassifier(\n", @@ -1335,9 +1324,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "bag_clf.fit(X_train, y_train)\n", @@ -1359,7 +1346,25 @@ ] } ], - "metadata": {}, + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.3" + } + }, "nbformat": 4, "nbformat_minor": 2 }