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

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