Welcome

.title[
# Welcome
]
.author[
### Machine Learning with R <a href='https://therbootcamp.github.io'> The R Bootcamp @ DHLab </a> <a href='https://therbootcamp.github.io/AML_2022DHLab/'> </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>
]
.date[
### September 2022
]

---

<div class="my-footer">
 
 
 <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/">
 
 Machine Learning with R @ DHLab | September 2022
 
 </a>
 
 </div>

---

# Aim

> The aim of this workshop is enable you to thoughtfully implement supervised machine learning methods, using tidymodels (day 1) and keras (day 2) in R.

]

]

---

# What is machine learning?

Machine learning is...

]

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

]

---

# What is machine learning?

Machine learning is...

...a <high>field of artificial intelligence</high>...

...that uses <high>statistical techniques</high>...

...to allow computer systems to <high>"learn"</high>,...

...i.e., to progressively <high>improve performance</high> on a specific task...

...from small or large amounts of <high>data</high>,...

....<high>without being explicitly programmed</high>....

....with the goal to <high>discover structure</high> or </high>improve decision making and predictions</high>.

]

]

---

# 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>

]

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

]

---

# Supervised learning

<ul>
 <li class="m1"><high>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>

]

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

]

---

# 2 types of supervised problems

There are two types of supervised learning problems typically can be approached <high>using the same model</high>.

Regression

Regression problems involve the <high>prediction of a quantitative feature</high>.

E.g., predicting the cholesterol level as a function of age.

Classification

Classification problems involve the <high>prediction of a categorical feature</high>.

E.g., predicting the origin of chest pain as a function of age and heart attack risk.

]

]

---

# Three supervised algorithms

---

# Neural networks

<img src="image/neural_nets.png"> 
adapted from <a href="asimovinstitute.com">asimovinstitute.com</a>

---

# ML in R

<ul>
 <li class="m1">R has advanced tremendously with respect to ML.</li>
 <li class="m2">There exist <high>powerful and user-friendly</high> tools for all ML steps and algorithms.</li>
</ul>

]

]

---

# 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>

]

]

---

# 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>

]

<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>

]

---

# Practicals

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

]

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

]

---

1. Who are you?

2. Why do you want to learn about ML with R?

3. How much experience do you have with ML and R?

4. What's your favorite algorithm (ML or non-ML)?

]

<img src="image/intro.jpg" style="width:500px">
 
from <a href="https://unsplash.com/photos/SymZoeE8quA">unsplash.com</a>

]

---

<h1><a href=https://therbootcamp.github.io/ML_2022DHLab/index.html>Schedule</a></h1>