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Computer Vision Group
TUM Department of Informatics
Technical University of Munich

Technical University of Munich

Home Teaching Summer Semester 2016 Probabilistic Graphical Models in Computer Vision (IN2329) (2h + 2h, 5 ECTS)


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teaching:ss2016:lecture_graphical_models [2016/06/22 10:15]
Csaba Domokos
teaching:ss2016:lecture_graphical_models [2016/07/18 11:03]
Csaba Domokos
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 Directed and undirected graphical models Directed and undirected graphical models
-  * Bayesian ​Network +  * Bayesian ​network 
-  * Markov ​Random Field +  * Markov ​random field 
-  * Conditional ​Random Field+  * Conditional ​random field
 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 ​Support Vector Machines+  * Structured ​support vector machine
 Exact MAP inference methods for MRFs Exact MAP inference methods for MRFs
-  * Belief propagation on trees: ​max-sum algorithm+  * Belief propagation on trees: ​Max-sum algorithm
   * Binary graph cuts   * Binary graph cuts
   * Branch-and-mincut   * Branch-and-mincut

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Informatik IX
Chair of Computer Vision & Artificial Intelligence

Boltzmannstrasse 3
85748 Garching