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‎0 - Introduction/pres.pdf

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‎0 - Introduction/pres.tex

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\makecell[{{p{0.27\textwidth}}}]{\centering $\{(x,y)\}$} & \makecell[{{p{0.27\textwidth}}}]{\centering $\{x\}$} & \makecell[{{p{0.27\textwidth}}}]{\centering $\{(x,u,r,x')\}$}\\
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\hline
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\makecell[{{p{0.07\textwidth}}}]{\textbf{Sub-task}} &
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\makecell[{{p{0.27\textwidth}}}]{\centering Classification, Regression} & \makecell[{{p{0.27\textwidth}}}]{\centering Clustering, Density estimation, Dimensionnality reduction} & \makecell[{{p{0.27\textwidth}}}]{\centering Value estimation, Policy optimization}\\
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\makecell[{{p{0.27\textwidth}}}]{\centering Classification, Regression} & \makecell[{{p{0.27\textwidth}}}]{\centering Clustering, Density estimation, Dimensionality reduction} & \makecell[{{p{0.27\textwidth}}}]{\centering Value estimation, Policy optimization}\\
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\hline
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\makecell[{{p{0.07\textwidth}}}]{\textbf{Algo ex.}} &
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\makecell[{{p{0.27\textwidth}}}]{\centering Neural Networks, SVM, Random Forests} & \makecell[{{p{0.27\textwidth}}}]{\centering k-means, PCA, HCA} & \makecell[{{p{0.27\textwidth}}}]{\centering Q-learning}\\
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~\\
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\underline{Ability to fit the training data (regression):}
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\begin{itemize}
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\item Mean Square Error, \href{https://en.wikipedia.org/wiki/Coefficient_of_determination}{coeffient of determination}.
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\item Mean Square Error, \href{https://en.wikipedia.org/wiki/Coefficient_of_determination}{coefficient of determination}.
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$$MSE = \frac{1}{N}\sum_i \left(y_i - f(x_i)\right)^2$$
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$$R^2 = 1-\frac{\sum_i \left(y_i - f(x_i)\right)^2}{\sum_i \left(y_i - \bar{y}\right)^2}$$
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\end{itemize}
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\item ML Notions:
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\begin{itemize}
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\item maximize margin
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\item minimize difference btw class distributions (cross-entropy)
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\item minimize difference btw class distributions (cross-entropy \href{https://en.wikipedia.org/wiki/Cross_entropy}{[link]})
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\end{itemize}
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\end{itemize}
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$$H(p,\hat{p}) = \sum_i p(x_i) \log (\hat{p}(x_i)) = \mathbb{E}_p \left(\log(\hat{p})\right)$$
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Back to the example of Predictive Maintenance tasks.
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\begin{itemize}
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\item Visualizing system state\\
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\hspace{1cm} $\rightarrow$ Dimensionnality reduction (Unsupervized learning)
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\hspace{1cm} $\rightarrow$ Dimensionality reduction (Unsupervized learning)
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\item Detecting anomalies\\
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\hspace{1cm} $\rightarrow$ Density estimation (Unsupervized learning)
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\item Predicting RUL or TTF\\

‎0 - Introduction/resources/1_What_is_etl.pdf

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‎0 - Introduction/resources/2_Coefficient_of_determination.pdf

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‎0 - Introduction/resources/3_Precision_and_recall.pdf

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‎0 - Introduction/resources/4_Receiver_operating_characteristic.pdf

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‎0 - Introduction/resources/5_Cross_entropy.pdf

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‎0 - Introduction/resources/6_Scikit-learn_table_of_contents.pdf

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‎1 - Unsupervized Learning/Unsupervized Learning.ipynb

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‎2 - Text data preprocessing/Text data preprocessing.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"toc": true
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},
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"source": [
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"<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
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"<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#1.-Load-the-data\" data-toc-modified-id=\"1.-Load-the-data-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>1. Load the data</a></span></li><li><span><a href=\"#2.-Filtering-out-the-noise\" data-toc-modified-id=\"2.-Filtering-out-the-noise-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>2. Filtering out the noise</a></span></li><li><span><a href=\"#3.-Even-better-filtering\" data-toc-modified-id=\"3.-Even-better-filtering-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>3. Even better filtering</a></span></li><li><span><a href=\"#4.-Term-frequency-times-inverse-document-frequency\" data-toc-modified-id=\"4.-Term-frequency-times-inverse-document-frequency-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>4. Term frequency times inverse document frequency</a></span></li><li><span><a href=\"#5.-Utility-function\" data-toc-modified-id=\"5.-Utility-function-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>5. Utility function</a></span></li></ul></div>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"execution_count": 1,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-10-08T13:52:39.328796Z",
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"start_time": "2018-10-08T13:52:39.172635Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"end_time": "2018-10-08T13:52:47.404302Z",
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"start_time": "2018-10-08T13:52:42.691109Z"
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}
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},
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"outputs": [],
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"source": [
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"from sklearn.feature_extraction.text import CountVectorizer\n",
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"execution_count": 3,
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"end_time": "2018-10-08T13:52:49.004851Z",
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"start_time": "2018-10-08T13:52:48.926879Z"
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}
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},
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"outputs": [
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"name": "stdout",
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},
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"cell_type": "code",
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"metadata": {},
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"execution_count": 4,
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"end_time": "2018-10-08T13:52:58.115382Z",
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"start_time": "2018-10-08T13:52:51.966698Z"
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}
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},
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"outputs": [],
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"source": [
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"from nltk import wordpunct_tokenize \n",
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 5,
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"metadata": {
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"end_time": "2018-10-08T13:52:58.148578Z",
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"start_time": "2018-10-08T13:52:58.117096Z"
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},
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"scrolled": true
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},
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"cell_type": "code",
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"execution_count": 7,
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"ExecuteTime": {
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"end_time": "2018-10-08T13:53:01.283024Z",
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"start_time": "2018-10-08T13:53:01.261090Z"
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},
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"scrolled": false
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"outputs": [
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"execution_count": 7,
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"ExecuteTime": {
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"end_time": "2018-10-08T13:53:06.126250Z",
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"start_time": "2018-10-08T13:53:06.102174Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(2893, 14279)"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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},
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"cell_type": "code",
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"ExecuteTime": {
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"end_time": "2018-10-08T13:53:08.606440Z",
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"start_time": "2018-10-08T13:53:08.601960Z"
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"execution_count": 9,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-10-08T13:53:15.239967Z",
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"start_time": "2018-10-08T13:53:11.837369Z"
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}
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},
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"outputs": [],
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"source": [
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"import load_spam\n",
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"cell_type": "code",
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"end_time": "2018-10-08T13:53:16.164299Z",
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"start_time": "2018-10-08T13:53:16.156978Z"
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},
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"outputs": [
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"name": "stdout",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.4"
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},
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"toc": {
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"base_numbering": 1,
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"nav_menu": {},
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"number_sections": true,
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"sideBar": true,
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"skip_h1_title": false,
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"title_cell": "Table of Contents",
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"title_sidebar": "Contents",
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"toc_cell": true,
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"toc_position": {
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"height": "calc(100% - 180px)",
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"left": "10px",
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"top": "150px",
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"width": "384px"
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},
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"toc_section_display": true,
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"toc_window_display": true
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}
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},
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"nbformat": 4,

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