This is a short tutorial on Gaussian Processes, Multi-fidelity Gaussian Processes, Gaussian Processes for Differential Equations, and Bayesian Optimization.

For more details, please refer to the following references: (https://maziarraissi.github.io/TutorialGP/)

1. Rasmussen, Carl Edward. "Gaussian processes for machine learning." (2006).

2. Raissi, Maziar, and George Karniadakis. "Deep Multi-fidelity Gaussian Processes." arXiv preprint arXiv:1604.07484 (2016).

3. Raissi, Maziar, and George Em Karniadakis. "Machine Learning of Linear Differential Equations using Gaussian Processes." arXiv preprint arXiv:1701.02440 (2017).

4. Raissi, Maziar, Paris Perdikaris, and George Em Karniadakis. "Inferring solutions of differential equations using noisy multi-fidelity data." arXiv preprint arXiv:1607.04805 (2016).

5. Shahriari, Bobak, et al. "Taking the human out of the loop: A review of bayesian optimization." Proceedings of the IEEE 104.1 (2016): 148-175.