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Gaussian Processes for Machine Learning

Number of credits: 9 hp

Examiner: Timo Koski

Course literature: Gaussian Processes for Machine Learning, Carl Edward Rasmussen and Christopher K. I. Williams MIT Press, 2006.

Course contents:
1 Introduction
1.1 Introduction to Bayesian Modelling
2 Regression
3 Classification
Multi-class Laplace Approximation
4 Covariance Functions
5 Model Selection and Adaptation of Hyperparameters
6 Relationships between GPs and Other Models
6.1 Reproducing Kernel Hilbert Spaces
6.2 Regularization
6.3 Spline Models
6.4 Support Vector Machines
6.5 Least-Squares Classification
6.6 Relevance Vector Machines
7 Theoretical Perspectives
7.1 The Equivalent Kernel
7.2 Asymptotic Analysis
7.3 Average-case Learning Curves
7.4 PAC-Bayesian Analysis
7.5 Comparison with Other Supervised Learning Methods
7.6 Appendix: Learning Curve for the Ornstein-Uhlenbeck Process

Organisation: Seminars and computer exercises.

Examination: Home exam.

Prerequisites:: TAMS47 or TAMS46. En tidigare kurs i statistisk inlärningsteori eller statistisk inferens inte nödvändig.

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Last updated: 2014-04-29