Machine Learning in Materials Science (MLinMS) - Materials Informatics

Course Exercises - Winter Term 2024
Instructor: Christopher Kuenneth
Computational Materials Science, University of Bayreuth

Overview

Welcome to the exercises for the "Machine Learning in Materials Science" course! Below you'll find relevant instructions, tools, and notes to get started effectively with the coursework.

Python Dependency Management

This course utilizes uv for managing and tracking Python dependencies. Make sure to set up uv to easily manage environments and dependencies for working on the exercises. If you work on Coder, uv is already installed.

Exercises

The exercises are usually provided as Jupyter notebooks in the folder wt_25_ml_in_ms.

Running the Notebooks

You have three options to run the provided Jupyter notebooks:

1. Google Colab
   Some notebooks come with a button to launch directly in Google Colab. This is a convenient option if you prefer running notebooks on the cloud.

2. Coder on Galadriel through VSCode
   You'll get access to Galadriel through Coder during the tutorials and can use it for working on the exercises and your project. Access works through VSCode.

Notes

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• The course exercises are designed to be run either in Google Colab or locally on VSCode, depending on your preference.
• Make sure to have the appropriate Python environment set up for running the notebooks.