Subject description - XEP33SML
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Explanatory Notes
Instructions
Subject is included into these academic programs:
XEP33SML | Structured Model Learning | ||
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Roles: | S | Extent of teaching: | 2P+1S |
Department: | 13133 | Language of teaching: | EN |
Guarantors: | Franc V. | Completion: | ZK |
Lecturers: | Flach B., Franc V. | Credits: | 4 |
Tutors: | Flach B., Franc V. | Semester: | L |
Web page:
https://cw.fel.cvut.cz/wiki/courses/xep33sml/startAnotation:
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.Literature:
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. |
Requirements:
- Solid knowledge of of statistical machine learning (cf. BE4M33SSU) - Basic knowledge of Graphical Models (cf. XEP33GMM)Note:
URL: http://cw.felk.cvut.cz/doku.php/courses/xep33sml/start |
Program | Branch | Role | Recommended semester |
DOKP | Common courses | S | – |
DOKK | Common courses | S | – |
Page updated 17.1.2025 07:51:06, semester: Z,L/2024-5, Z/2025-6, Send comments about the content to the Administrators of the Academic Programs | Proposal and Realization: I. Halaška (K336), J. Novák (K336) |