courses:xep33sml:materials:lectures [CourseWare Wiki]

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		[Neal92], Rezende, 2014, Kingma, 2013
04	VF	Empirical Risk Minimization	, print	[Domke10] [Agarwal11] [Balcan11]
05	VF	Structured Output Linear Classifier and Perceptron	, print	[Collins02] Peceptron proof
06	VF	Learning max-sum classifier by Perceptron	, print	[Franc08]
07	VF	Structured Output Support Vector Machines	, print	[Tsochantaridis05]
08	VF	Cutting Plane Algorithm	, print	[Teo09]
09	VF	Learning Max-Sum classifier by SO-SVM	, print	[Taskar04] [Taskar04] [Franc08]
10	BF	Maximum Likelihood learning for MRFs I		[Fujishige (book)], [Zhang2015]
11	BF	Maximum Likelihood learning for MRFs II
12	BF	Variational Bayesian inference for DNNs
13	BF	Variational Autoencoders		[Doersch2016]
14	BF	Generative adversarial networks		[Arjovsky2017]