xep33sml -- Structured Model Learning

This advanced machine learning course covers learning and parameter estimation for structured models.It aims to communicate knowledge on theory and algorithms for the two currently most succesfull branches of structured model learning - statistical learning and structured output learning.

• 23.06.15 Results for the written exam are ready. Students can come now individually to pick them up.
• 14.05.15 The exam will be held in KN:E-128 on Tuesday 16th of June from 11:00 to 12:45.
• 30.04.15 Doodle poll for the exam date (closed on May, 13)
• 26.02.15 The lecture on Thursday, March 5 is canceled. Instead, the seminar will take place starting from 14:30. Assignments for the first seminar are available for download.

• Teachers: Boris Flach web-page and Vojtech Franc web-page
• Course format: (2/1)
• Schedule: SS14/15
• 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.I. Schlesinger and V. Hlaváč. Ten Lectures on Statistical and Structural Pattern Recognition. Kluwer Academic Publishers, 2002.
  • M.J. Wainwright and M.I. Jordan. New Directions in Statistical Signal Processing: From Systems to Brains, chapter A Variational Principle for Graphical Models. MIT Press, 2007.
  • 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.

• Links: discussion forum