Update README.md

README.md

@@ -1,17 +1,21 @@
 # Tutorial on introduction to Bayesian optimization
+
 [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)
 
 - [Download the repository](#download-the-repository)
 - [Getting started](#getting-started)
 - [Running the tutorial](#running-the-tutorial)
+- [Citing the tutorial](#citing-the-tutorial)
 
 ## Material for this tutorial
+
 - The theoretical lecture can be found here
-- The tutorial in slide form is here
+- The tutorial in slide form can be found [here](https://machine-learning-tutorial.github.io/bayesian-optimization)
 
 ## Download the repository
 
 ### Get the repository with Git
+
 You will need to have Git previously installed in your computer.
 To check if you have it installed, open your terminal and type:
 
@@ -27,6 +31,7 @@ brew install git
 ```
 
 #### Git installation in linux
+
 In Ubuntu/Debian
 
 ``` bash
@@ -40,20 +45,24 @@ sudo yum install git
 ```
 
 Once you have Git installed open your terminal, go to your desired directory, and type:
+
 ``` bash
 git clone https://github.com/machine-learning-tutorial/bayesian-optimization.git
 cd bayesian-optimization
 ```
 
 ### Get the repository with direct download
+
 Open your terminal, go to your desired directory, and type:
+
 ``` bash
 wget https://github.com/machine-learning-tutorial/bayesian-optimization/archive/refs/heads/main.zip
 unzip main.zip
 cd bayesian-optimization
 ```
 
 ## Getting started
+
 You need to install the dependencies before running the notebooks.
 
 ### Using conda
@@ -97,8 +106,13 @@ jupyter lab
 Alternatively, you can use supported Editor to run the jupyter notebooks, e.g. with VS Code.
 
 ## Citing the tutorial
-This tutorial is registered [Zenodo](https://zenodo.org/), which means that there is a DOI for each code release. 
+
+This tutorial is published in [Zenodo](https://zenodo.org/) with a DOI for each code release.
+
 Please use this DOI when citing this code:
 
 ## Disclaimer
+
 The content of this repository was developed by the [AI4Accelerators team](https://www.ibpt.kit.edu/AI4Accelerators.php) at the [Institute of Beam Physics and Technology (IBPT)](https://www.ibpt.kit.edu/), [Karlsruhe Institute of Technology](https://www.kit.edu/english/).
+
+The ARES environment was adapted from the original version, developed by [Jan Kaiser](https://github.com/jank324).