Subject description - XEP33SML
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Explanatory Notes
Instructions
Web page:
https://cw.fel.cvut.cz/wiki/courses/xep33sml/start
Anotation:
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:
Subject is included into these academic programs:
| 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) |