## Hands-on Tutorial B: Gaussian Process Regression (by Roman Krems)
Folder "B - Gaussian Process Regression (by Roman Krems)" contains:
- a file `H3O+.csv` with 6D potential energy surface (PES) for the molecule $H_3O^+$ (with six first columns listing the coordinates and the seventh - the energy),
- a Jupyter notebook `Gaussian_process_regression.ipynb` with the exemplary code to build Gaussian process (GP) regression of 1D synthetic data with simple kernels, and three tasks:
  1. To extend the code to do GP regression of the 6D PES of $H_3O^+$.
  2. To use four different kernels (provided by sklearn).
  3. To build more complex kernels using the Bayesian Infomation Criterion (BIC) as model selection metric (as described in lectures).
Solutions of the tasks were prepared by Anna Dawid, are only exemplary, and have no ambitions of being the optimal solutions.