Warning
This page is located in archive. Go to the latest version of this course pages. Go the latest version of this page.

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
11 BF Maximum Likelihood learning for MRFs II
12 BF Variational Bayesian inference for DNNs
13 BF Variational Autoencoders I
14 BF Variational Autoencoders II
courses/xep33sml/materials/lectures.txt · Last modified: 2021/02/23 10:38 by flachbor