/Gaussian-Processes-for-Machine-Learning

Gaussian Processes for Machine Learning

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Advanced Seminar Course: Gaussian Processes for Machine Learning

Gaussian Processes for Regression

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

An example of Bayesian Linear Regression with sin transformation of data

An example of Gaussian Process Regression

References

  • 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.