What is ML

# What is ML
### <a href='https://therbootcamp.github.io'> Data analytics for Psychology and Business </a> <a href='https://cdsbasel.github.io/dataanalytics_2021/menu/materials.html'> </a>  <a href='https://cdsbasel.github.io/dataanalytics_2021/'> </a>  <a href='mailto:dirk.wulff@unibas.ch'> </a> 
### April 2021

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 Data analytics for Psychology and Business | April 2021
 
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# What do you think?

No Googling :)

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# What is machine learning?

<ul>
 <li class="m1">Machine learning is...</li> 
 <ul class="level">
 <li>...an <high>area of artificial intelligence</high>...</li> 
 <li>...that uses <high>statistical methods</high>...</li> 
 <li>...to computers to <high>learn</high>...</li> 
 <li>...i.e., to iteratively <high>improve</high> task performance...</li> 
 <li>...on the basis of available <high>data</high>.</li>
 </ul>
</ul>

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<img src="image/ml_robot.jpg" height=380px> 
from <a href="https://medium.com/@dkwok94/machine-learning-for-my-grandma-ca242e97ef62">medium.com</a>

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# ML's origin

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# Types of machine learning tasks

<ul>
 <li class="m1">There are many types of machine learning tasks, each of which call for different models.</li>
 <li class="m2"><high>We will focus on supervised machine learning</high>.</li>
</ul>

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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>
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 Term
 </td>
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 Definition
 </td> 
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 Example
 </td> 
</tr>
<tr>
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 Case (Fall)
 </td>
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 Die <high>Unit of observation</high> in data.
 </td> 
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 A patient, a site, etc.
 </td> 
</tr>
<tr>
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 Feature (variable, predictor)
 </td>
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 A measurable <high>property</high> of cases. 
 </td> 
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 Age, temperature, country, etc.
 </td> 
</tr>
<tr>
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 Criterion (variable, criterion)
 </td>
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 The <high>Feature</high> that you want to <high>predict</high>.
 </td> 
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 Heart attack, sales, etc.
 </td> 
</tr>
<tr>
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 Data
 </td>
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 Typically <high>rectangular</high> with <high>Cases in rows</high> and <high>Features in columns</high>.
 </td> 
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 <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>

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

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# 3 key (supervised) models

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# Unsupervised learning

<ul>
 <li class="m1">Analyses relationships between cases or features to <high>discover hidden structures</high>.</li> 
 <ul class="level">
 <li><high>Dimensionality reduction</high>: Group features on the basis of correlations into a smaller number of synthetic features.</li> 
 <li><high>Clustering</high>: Group cases on the basis of similarities/distances into clusters.</li>
 </ul>
</ul>

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# Reinforcement learning

<ul>
 <li class="m1">Domain <high>between supervised and unsupervised</high> learning.</li> 
 <li class="m2"><high>Learns iteratively</high> on the basis of (minimal) feedback.</li> 
 <li class="m3">Used in:</li>
 <ul class="level"> 
 <li>Modell fitting.</li> 
 <li>Robotics.</li> 
 <li>Games: Chess, Go, or Mario Kart.</li>
 </ul>
</ul>

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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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<h1><a href="https://raw.githubusercontent.com/therbootcamp/ML-DHLab/main/TheRBootcamp.zip">Project</a></h1>

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