courses:xep33sml:materials:lectures [CourseWare Wiki]

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