The repository includes the presentation, paper and jupyter notebook covering the topic "Gaussian Processes for Regression" at Technical University of Munich within the scope of seminar "Gaussian Processes for Machine Learning".
- Course code: MAHS21W08
- Examiner: Prof. Dr. Michael Wolf
- Credits: 3 ECTS
- Presentation date: 03.02.2022
- M. Kanagawa, P. Hennig, D. Sejdinovic, and B. K. Sriperumbudur. Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences. 2018. arXiv: 1807.02582 [stat.ML].
- F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. “Scikit-learn: Machine Learning in Python.” In: Journal of Machine Learning Research 12 (2011), pp. 2825–2830.
- C. E. Rasmussen and C. K. I. Williams. Gaussian processes for machine learning. Adaptive computation and machine learning. MIT Press, 2006, pp. I–XVIII, 1–248. isbn: 026218253X.