Subject description - XEP33SML

Summary of Study | Summary of Branches | All Subject Groups | All Subjects | List of Roles | Explanatory Notes               Instructions
XEP33SML Structured Model Learning
Roles:S Extent of teaching:2P+1S
Department:13133 Language of teaching:EN
Guarantors:Flach B. Completion:ZK
Lecturers:Flach B., Franc V. Credits:4
Tutors:Flach B., Franc V. Semester:L

Web page:


This advanced machine learning course covers learning and parameter estimation for structured models like Markov Random Fields, Belief Networks and (stochastic) Deep Neural Networks.

Study targets:

The course aims to communicate knowledge on theory and algorithms for the two currently most successful branches of structured model learning - statistical learning and structured output learning.

Course outlines:

(1) Markov Random Fields & Gibbs Random Fields
(2) Belief Networks & Stochastic Neural Networks
(3) Learning of structured output classifiers by Perceptron
(4) Structured Output Support Vector Machines
(5) Learning max-sum classifiers by SO-SVM
(6) Optimization methods for SO-SVM
(7) Maximum Likelihood learning for MRFs
(8) Variational Autoencoders
(9) Variational Bayesian inference for DNNs
(10) Generative adversarial networks

Exercises outline:

The seminars will be dedicated to discussions and deepening the knowledge acquired at the lectures.


1. B. Taskar, C. Guestrin, and D. Koller. Maximum-margin markov networks. In Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA, 2004.
2. I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun. Large margin methods for structured and interdependent output variables. Journal of Machine Learning Research, 6:1453-1484, Sep. 2005.
3. V. Franc and B. Savchynskyy. Discriminative learning of max-sum classifiers. Journal of Machine LearningResearch, 9(1):67-104, January 2008. ISSN 1532-4435.
4. M.J. Wainwright and M.I. Jordan. Graphical Models, Exponential Families, and Variational Inference. Foundations and Trends in Machine Learning, 1(1-2):1-305, 2008.


- Solid knowledge of of statistical machine learning (cf. BE4M33SSU) - Basic knowledge of Graphical Models (cf. XEP33GMM)



Subject is included into these academic programs:

Program Branch Role Recommended semester
DOKP Common courses S
DOKK Common courses S

Page updated 15.6.2024 17:51:22, semester: Z/2024-5, Z,L/2023-4, Send comments about the content to the Administrators of the Academic Programs Proposal and Realization: I. Halaška (K336), J. Novák (K336)