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Syllabus

Lect. Lecturer Topic Pdf
01 BF Markov Random Fields & Gibbs Random Fields
02 BF The most probable realisation of a GRF
03 BF Belief Networks & Stochastic Neural Networks
04 VF Discriminative structured output learning, Perceptron algorithm I
05 VF Discriminative structured output learning, Perceptron algorithm II
06 VF Learning max-sum classifier by Perceptron
07 VF Structured Output Support Vector Machines I
08 VF Structured Output Support Vector Machines II
09 VF Learning Max-Sum classifier by SO-SVM +Joachims05
10 BF Maximum Likelihood learning for MRFs I
11 BF Maximum Likelihood learning for MRFs II
12 BF Variational Bayesian inference for DNNs
13 BF Variational Autoencoders
14 BF Generative adversarial networks
courses/xep33sml/materials/lectures.txt · Last modified: 2019/05/20 22:45 by flachbor