Online resource of a practical machine learning course in the Department of Materials at Imperial College London.
You have the option to browse the files or download the complete folder using the green clone or download button on the top right of the screen (zip file).
Machine Learning for Materials (MATE70026) provides an introduction to statistical research tools for materials theory and simulation. It is aimed at senior undergraduate or junior postgraduate students.
You will consider how composition-structure-property information in materials science can be represented in a form suitable for machine learning. You will then build, train, and evaluate your own models using public tools and open datasets.
A hybrid teaching style will be followed with a mixture of lectures and assignments. The course assumes a basic working knowledge of the Python 3 programming language.
You can view the site at https://aronwalsh.github.io/MLforMaterials
To build a local copy, first install Jupyter Book:
pip install -U jupyter-book
then enter the repository and run
jupyter-book build .
This course was developed by Aron Walsh with the assistance of Anthony Onwuli, Zhenzhu Li. Thanks to Calysta Tesiman for testing the first draft of the Jupyter notebooks.