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teaching:ss2016:lecture_graphical_models [2016/06/20 09:47] domokos |
teaching:ss2016:lecture_graphical_models [2016/07/18 11:03] domokos |
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Directed and undirected graphical models | Directed and undirected graphical models | ||
- | * Bayesian | + | * Bayesian |
- | * Markov | + | * Markov |
- | * Conditional | + | * Conditional |
Parameter learning for MRF and CRF models | Parameter learning for MRF and CRF models | ||
* Gradient based optimization | * Gradient based optimization | ||
* Stochastic gradient descent | * Stochastic gradient descent | ||
- | * Structured | + | * Structured |
Exact MAP inference methods for MRFs | Exact MAP inference methods for MRFs | ||
- | * Belief propagation on trees: | + | * Belief propagation on trees: |
* Binary graph cuts | * Binary graph cuts | ||
* Branch-and-mincut | * Branch-and-mincut | ||
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== Exam == | == Exam == | ||
The exam will be oral. | The exam will be oral. | ||
+ | Please **do not forget** to register to the exam by 30.06.2016 in TUM Online. | ||
+ | ** Location:** Room 02.09.023\\ | ||
+ | ** 1st Date:** 19 July 2016\\ | ||
+ | ** 2nd Date:** 2 August 2016\\ | ||
+ | |||
+ | Please contact us per email if you want to reserve a specific time slot. | ||
+ | For more details visit the [[teaching: | ||
== Recommended literature == | == Recommended literature == |