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.

• 26.06.20 Assignments for the exam
• 03.06.20 Assignments for the early track exam
• 07.03.20 Interactive TeX board
• 19.03.20 Dear PhD students, we decided to go for the BigBlueButton option. You will receive an e-mail with an invitation link shortly before the meeting next Tuesday, 12:45. You may have a look at this youtube video, which shows how BBB will work for you as a student.
• 17.03.20 Dear PhD Students, as you know teaching at CTU is suspended till 22.03. We will resume teaching in distant mode starting next week, i.e. on Tuesday, 24.03. For this we will use either a BRUTE plugin based on bigbluebutton (in preparation) or Google hangout. We will inform you as soon as we know more. Next week we will discuss contents of lecture 3.
• 25.02.20 Tuesday next week (3.3.) we will discuss contents of lecture 2 and solutions of the assignments of the first seminar

• 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: SS20
• 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
  • Bogdan Savchynskyy, Discrete Graphical Models - an Optimization Perspective, Foundations and Trends in Computer Graphics and Vision 11(3-4)160-429, 2019

• Links: discussion forum