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teaching:ss2011:smlcv2011 [2011/04/27 15:13] nieuwenh |
teaching:ss2011:smlcv2011 [2011/04/27 15:28] 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 | ||
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| ==== Exercises: ==== | ==== Exercises: ==== | ||
| - | ** Location:** 02.09.023\\ ** Time and Date:** every Tuesday, 2.15 pm \\ ** Organization: | + | ** Location:** 02.09.023\\ ** Time and Date:** every other Tuesday, 2.15 pm \\ ** Organization: |
