Structured Model Learning

Course guarantor:

Vojtěch Franc

Lecturer:

Boris Flach, Vojtěch Franc

Tutor:

Boris Flach, Vojtěch Franc

Supervisor:

Department of Cybernetics

Synopsis:

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

Requirements:

- Solid knowledge of of statistical machine learning (cf. BE4M33SSU)

- Basic knowledge of Graphical Models (cf. XEP33GMM)

Syllabus of lectures:

(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

Syllabus of tutorials:

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

Study Objective:

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.

Study materials:

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.

Note:

Further information:

https://cw.fel.cvut.cz/wiki/courses/xep33sml/start

Time-table for winter semester 2024/2025:

Time-table is not available yet

Time-table for summer semester 2024/2025:

	06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon
Tue	roomKN:E-128 Flach B. Franc V. 12:45–14:15 (lecture parallel1) Karlovo nám. roomKN:E-128 Flach B. Franc V. 14:30–16:00 EVEN WEEK (lecture parallel1 parallel nr.101) Karlovo nám. roomKN:E-126 Flach B. Franc V. 12:45–14:15 (lecture parallel1) Karlovo nám.
Wed
Thu
Fri

The course is a part of the following study plans:

• Doctoral studies, daily studies (compulsory elective course)
• Doctoral studies, combined studies (compulsory elective course)
• Doctoral studies, structured daily studies (compulsory elective course)
• Doctoral studies, structured combined studies (compulsory elective course)