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 [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]