Introduction

# Introduction
### Applied Machine Learning with R <a href='https://therbootcamp.github.io'> The R Bootcamp @ AMLD </a> <a href='https://therbootcamp.github.io/AML_2020AMLD/'> </a>  <a href='https://therbootcamp.github.io'> </a>  <a href='mailto:therbootcamp@gmail.com'> </a>  <a href='https://www.linkedin.com/company/basel-r-bootcamp/'> </a>
### January 2020

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 <img src="https://raw.githubusercontent.com/therbootcamp/therbootcamp.github.io/master/_sessions/_image/by-sa.png" height=14 style="vertical-align: middle"/>
 
 <a href="https://therbootcamp.github.io/">
 
 
 www.therbootcamp.com
 
 
 </a>
 <a href="https://therbootcamp.github.io/">
 
 Applied Machine Learning with R @ AMLD | January 2020
 
 </a>
 
 </div>

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# Slides and Materials

Find our Materials under:

<a href="https://therbootcamp.github.io/AML_2020AMLD/">WETEACHR.COM/AML_2020AMLD</a>

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# We teach R

> We are Swiss-based data scientists who have jointly spent over 25 years doing data science with R for both research and industry. Our goal is to help you learn the skills needed to discover and communicate insights from data and join the data revolution.

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 <a href="www.dirkwulff.org"><img border="0" alt="W3Schools" src="https://therbootcamp.github.io/img/team/1.png" height="160" style="border-radius:20%"></a>
 
 Dirk&nbsp;&nbsp;
 <a href="www.dirkwulff.org"></a>
 <a href="https://github.com/dwulff"></a>
 
 </th>
 <td style="vertical-align:top">
 
 <a href="https://psychologie.unibas.ch/en/persons/markus-steiner/about-me/"><img border="0" alt="W3Schools" src="https://therbootcamp.github.io/img/team/2.png" height="160" style="border-radius:20%"></a>
 
 Markus&nbsp;&nbsp; 
 <a href="https://github.com/mdsteiner"></a>
 
 </th>

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# Aim

> The aim of this workshop is enable you to thoughtfully implement machine learning methods, using R, to make data-driven decisions.

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# Data-driven decisions

<ul>
 <li class="m1">Predicting Heart Attacks
 
 <ul class="level">
 <li>You are an intake nurse at an emergency room.</li>
 <li>A patient comes in complaining of chest pain and thinks he is having a heart attack.</li>
 <li><high>How do you decide whether or not the patient is really having a heart attack?</high></li>
 </ul>
 </li>
 <li class="m2">Predicting Sales
 
 <ul class="level">
 <li>You are an analyst at a retail corporation.</li>
 <li>The executive team is considering whether or not to open a new retail location in Basel.</li>
 <li><high>How can you predict what the sales of the new store would be?</high></li>
 </ul>
 </li>
</ul>

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<img src="image/chestpain.jpg" height=180px width=260px> 
from <a href="https://medium.com/@dkwok94/machine-learning-for-my-grandma-ca242e97ef62">medium.com</a>

<img src="image/storefront.jpg" height=180px width=260px> 
from <a href="https://thirdmanrecords.com/locations/detroit-storefront">thirdmanrecords.com</a>

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# Machine learning is data-driven

Data-driven, ML-based heart rate prediction

Based on <high>data</high> from past patients <high>at this hospital</high>, a <high>regression model</high>, using the patient's <high>age, cholesterol level, and ecg</high>, <high>predicts</high> the probability that this patient is having a heart attack is only <high>45%</high>.

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<img src="image/chestpain_quote_ml.png" height=180px> 
from <a href="https://medium.com/@dkwok94/machine-learning-for-my-grandma-ca242e97ef62">medium.com</a>

<img src="image/storefront_quote_ml.png" height=180px> 
from <a href="https://thirdmanrecords.com/locations/detroit-storefront">thirdmanrecords.com</a>

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# Types of machine learning tasks

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<img src="image/mltypes.png" height=500px> 
from <a href="image/mltypes.png">amazonaws.com</a>

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# Data terminology

<tr>
 <td bgcolor="white">
 Term
 </td>
 <td bgcolor="white">
 Definition
 </td> 
 <td bgcolor="white">
 Example
 </td> 
</tr>
<tr>
 <td bgcolor="white">
 Case
 </td>
 <td bgcolor="white">
 A specific <high>observation</high> of data.
 </td> 
 <td bgcolor="white">
 A patient, a site, etc.
 </td> 
</tr>
<tr>
 <td bgcolor="white">
 Feature
 </td>
 <td bgcolor="white">
 An measurable <high>property</high> of cases. Also called predictors. 
 </td> 
 <td bgcolor="white">
 Age, temperature, country, etc.
 </td> 
</tr>
<tr>
 <td bgcolor="white">
 Criterion
 </td>
 <td bgcolor="white">
 The <high>feature</high> that you want to <high>predict</high>.
 </td> 
 <td bgcolor="white">
 Heart attack, sales, etc.
 </td> 
</tr>
<tr>
 <td bgcolor="white">
 Data
 </td>
 <td bgcolor="white">
 Typically <high>rectangular</high> representation of cases (rows) and features (columns).
 </td> 
 <td bgcolor="white">
 <mono>.csv</mono>, <mono>.xls</mono>, <mono>.sav</mono>, etc.
 </td> 
</tr>
</table>

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# Supervised learning

<ul>
 <li class="m1">The <high>dominant type</high> of machine learning.</li>
 <li class="m2">Supervised learning uses <high>labeled data</high> to learn <high>a model</high> that relates the criterion to the features.</li>
</ul>

Verbal model

<mono>if cp (chest pain) is not a (asymptomatic) and age is larger than 60 then high probability of hearth attack, otherwise low probability.</mono>

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<img src="image/supervised.png"> 

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# 2 types of supervised problems

<ul>
 <li class="m1"><high>Regression</high>
 
 <ul class="level">
 <li>Regression problems involve the <high>prediction of a quantitative feature</high>.</li>
 <li>E.g., predicting the cholesterol level as a function of age. </li>
 </ul>
 </li>
 <li class="m2"><high>Classification</high>
 
 <ul class="level">
 <li>Classification problems involve the <high>prediction of a categorical feature</high>. </li>
 <li>E.g., predicting the origin of chest pain as a function of age and heart attack risk. </li>
 </ul>
 </li>
</ul>

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# Unsupervised learning

<ul>
 <li class="m1">Analyzes the relationships among cases (<high>clustering</high>) or among features (<high>dimensionality reduction</high>) to <high>discover structures</high> such as groups or meta-features.</li>
</ul>

<tr>
 <td bgcolor="white">
 Approach
 </td>
 <td bgcolor="white">
 Description
 </td> 
 <td bgcolor="white">
 Example
 </td> 
</tr>
<tr>
 <td bgcolor="white">
 Clustering
 </td>
 <td bgcolor="white">
 Analyze distances between cases to identify <high>clusters of homogeneous cases</high>.
 </td> 
 <td bgcolor="white">
 Types of customers or patients.
 </td> 
</tr>
<tr>
 <td bgcolor="white">
 Dimension- ality reduction
 </td>
 <td bgcolor="white">
 Analyze correlations between features to identify <high>higher order features</high>. 
 </td> 
 <td bgcolor="white">
 Dimensions of personality or user experience.
 </td> 
</tr>
</table>

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# Reinforcement learning

<ul>
 <li class="m1"><high>Learns iteratively</high> from minimal supervision provided by <high>performance feedback</high>.</li>
 <li class="m2">RL is closely <high>related to psychological theories of learning</high>.</li>
</ul>

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<tr>
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 Application
 </td>
 <td bgcolor="white">
 Description
 </td> 
</tr>
<tr>
 <td bgcolor="white">
 Model fitting
 </td>
 <td bgcolor="white">
 Iteratively <high>change model parameters</high> to improve prediction. 
</tr>
<tr>
 <td bgcolor="white">
 Robot movements
 </td>
 <td bgcolor="white">
 Iteratively <high>change movement</high> patterns to increase pancake-catch probability. 
</tr>
<tr>
 <td bgcolor="white">
 Games
 </td>
 <td bgcolor="white">
 Iteratively <high>change controller input</high> patterns to improve Mario Kart racing time. 
</tr>
</table>

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<img src="image/roboarm.gif" width=320px> 
from <a href="https://giphy.com/explore/reinforcement-learning">giphy.com</a>

<img src="image/mariokart.gif" width=320px> 
from <a href="https://blogs.nvidia.com/blog/2017/04/14/tensorkart-ai-mario-kart/">nvidia.com</a>

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# What we will cover

<ul>
 <li class="m1"><high>Core steps</high>
 
 <ul class="level">
 <li>Fitting</li>
 <li>Prediction</li>
 <li>Tuning</li>
 </ul>
 </li>
 <li class="m2"><high>Supervised learning</high>
 
 <ul class="level">
 <li>Regression</li>
 <li>Classification</li>
 </ul>
 </li>
 <li class="m3"><high>Algorithms</high>
 
 <ul class="level">
 <li>Regression</li>
 <li>Decision trees</li>
 <li>Random forests</li>
 </ul>
 </li>
</ul>

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<img src="image/ml_robot.jpg" > 
from <a href="https://medium.com/@danielgross/managing-machine-learning-43fe870b62be">medium.com</a>

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# Machine learning is more than algorithms

<img src="image/mlsteps.png" height=440px> 
from <a href="https://www.houseofbots.com/images/news/11493/cover.png">houseofbots.com</a>

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# `caret`

<ul>
 <li class="m1">The <high>C</high>lassification <high>A</high>nd <high>RE</high>gression <high>T</high>raining package is a meta-package to streamline the application of R's best machine learning tools. </li>
 <li class="m2"><mono>caret</mono> facilitates...
 
 <ul class="level">
 <li><high>data pre-processing</high></li>
 <li><high>fitting, tuning, & model prediction</high></li>
 </ul>
 </li>
 <li class="m3">Provides access to dozens of algorithms
 
 <ul class="level">
 <li><high>regression, decision trees, random forests</high></li>
 <li>neural nets, AdaBoost, support vector machines, etc.</li>
 </ul>
 </li>
</ul>

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# Schedule

<ul>
 <li class="m1"><high>Schedule</high>
 
 <ul class="level">
 <li>Intro to R</li>
 <li>3 blocks on ML with R</li>
 </ul>
 </li>
 <li class="m2"><high>Block</high>
 
 <ul class="level">
 <li>Slide-based introduction</li>
 <li>Many exercises</li>
 </ul>
 </li>
</ul>

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# Introduction

<ul>
 <li class="m1"><high>Introduction</high>
 
 <ul class="level">
 <li>30-45 min</li>
 <li>Concepts & Code examples</li>
 </ul>
 </li>
 <li class="m2"><high>Materials</high>
 
 <ul class="level">
 <li>Permanently <a href="https://therbootcamp.github.io/EDA_2019CSS/">available online</a></li>
 <li>PDFs will be available later</li>
 </ul>
 </li>
</ul>

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<img src="https://img.freepik.com/free-vector/business-presentation_23-2147511785.jpg?size=338&ext=jpg" style="width:250px">
 
from <a href="www.Freepik.com">Freepik.com</a>

<img src="https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcR5DZ7eRkgxeFuEMEtuVC9YHz2v0STxeQ9YJyW2T3PbXGIci_2Lig" style="width:250px">
 
from <a href="www.Freepik.com">Freepik.com</a>

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# Practicals

<ul>
 <li class="m1"><high>Write your own scripts</high>
 
 <ul class="level">
 <li>20 - 50 Aufgaben</li>
 <li>Increasingly getting difficult.</li>
 <li>Follow your own tempo.</li>
 <li>Answers are added later.</li>
 </ul>
 </li>
</ul>

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Example: <a href="https://therbootcamp.github.io/AML_2020AMLD/_sessions/Fitting/Fitting_practical.html">Fitting Practical </a>

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<h1><a href=https://therbootcamp.github.io/AML_2020AMLD/index.html>Schedule</a></h1>