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Syllabus

Lect. Lecturer Topic Pdf Additional reading
01 BF Markov Random Fields & Gibbs Random Fields
02 BF The most probable realisation of a GRF [Savchynskyy19]
03 BF Belief Networks & Stochastic Neural Networks
04 VF Empirical Risk Minimization
05 VF Structured Output Linear Classifier and Perceptron
06 VF Learning max-sum classifier by Perceptron
07 VF Structured Output Support Vector Machines
08 VF Cutting Plane Algorithm
09 VF Learning Max-Sum classifier by SO-SVM
10 BF Maximum Likelihood learning for MRFs I updated on 4.5.22
11 BF Maximum Likelihood learning for MRFs II
12 BF Variational Autoencoders
courses/xep33sml/materials/lectures.txt · Last modified: 2022/05/17 11:30 by flachbor