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xep33sml -- Structured Model Learning


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


  • 28.05.19 The exam will take place on Friday, June 7, 10:00-11:30am in KN:G-205. You are allowed to bring and use a handwritten A4 page (one sided) with your notes.
  • 13.05.19 The lecture on Tue, 14.05. will take place in KN:G-205


  • Teachers: Boris Flach web-page and Vojtech Franc web-page
  • Prerequisites: solid knowledge of of statistical machine learning, basic knowledge of Graphical Models
  • Course format: (2/1)
  • Schedule: SS19
  • Lectures: See here for the syllabus
  • Seminars: Exercises/Assignments will be provided prior to each seminar class. You are supposed to work on them before the seminar. Solutions will be discussed at the seminar class.
  • Grading/Credits: Written exam, 4CP
  • Textbooks and References:
    • B. Taskar, C. Guestrin, and D. Koller. Maximum-margin markov networks. In Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA, 2004.
    • 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.
    • V. Franc and B. Savchynskyy. Discriminative learning of max-sum classifiers. Journal of Machine LearningResearch, 9(1):67-104, January 2008. ISSN 1532-4435.
    • 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.
    • Stan Z. Li, Markov Random Field Modeling in Image Analysis, Springer, 2009
    • Daphne Koller and Nir Friedman, Probabilistic Graphical Models, MIT Press, 2009
    • Andrew Blake, Pushmeet Kohli and Carsten Rother (editors), Markov Random Fields for Vision and Image Processing, MIT Press, 2011


courses/xep33sml/start.txt · Last modified: 2019/05/28 15:32 by flachbor