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teaching:ss2011:smlcv2011 [2011/04/27 15:13] nieuwenh |
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* necessary basics in measure theory and statistics, e.g. measures, distributions, | * necessary basics in measure theory and statistics, e.g. measures, distributions, | ||
- | * parametric and non-parametric density estimation, e.g. Parzen density estimator, mixture of Gaussians, EM-algorithm | + | * density estimation (parametric and non-parametric) and sampling methods such as Parzen |
* subspace methods such as principal component analysis, idependent component analysis, linear discriminant analysis, e.g. with application to face recognition | * subspace methods such as principal component analysis, idependent component analysis, linear discriminant analysis, e.g. with application to face recognition | ||
- | * density estimation and sampling methods such as Parzen density estimation and particle filtering, e.g. with application to image segmentation and tracking | ||
* learning and classification approaches such as Support Vector Machines, Neural Networks, Graphical Models and Dictionary Learning | * learning and classification approaches such as Support Vector Machines, Neural Networks, Graphical Models and Dictionary Learning | ||