"description":"In this tutorial we will introduce decision trees. We go through a toy model introducing the SKLearn API. We then discuss piece by piece the different theoretical aspects of trees. We then move to training a regression tree and classification tree on different datasets related to materials sceience. We end the tutorial by covering random forests and bagging classfifers.",
"title":"Kernel Ridge Regression for Materials Property Prediction: A Tutorial Introduction",
"description":"In this tutorial, we'll explore the application of kernel ridge regression to the prediction of materials properties. We will begin with a largely informal, pragmatic introduction to kernel ridge regression, including a rudimentary implementation, in order to become familiar with the basic terminology and considerations. We will then discuss representations, and re-trace the NOMAD 2018 Kaggle challenge.",
"title":"Symbolic regression via compressed sensing: a tutorial",
"description":"In this tutorial we will show how to find descriptive parameters to predict materials properties using symbolic regrression combined with compressed sensing tools. The relative stability of the zincblende (ZB) versus rocksalt (RS) structure of binary materials is predicted and compared against a model trained with kernel ridge regression.",
