xep33sml -- Structured Model Learning

Overview

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.

News

  • 10.01.2021 Dear PhD students, unfortunately for all of us, teaching in this semester will start in online mode. We will use the BigBlueButton platform (BBB), which runs safely and securely on local university servers. All you need for joining the virtual teaching rooms is a web-browser and a headset. If you are not familiar with this platform, please have a look at https://bigbluebutton.org/ and the demo videos provided there. We will schedule the meetings to start a few minutes ahead of the regular time, so that you can set up your gear and join them without hurry. You will receive invitation e-mails well before each scheduled meeting. The BBB platform is integrated in the faculty upload system BRUTE. Therefore you can also join the meetings via BRUTE. Please notice that for all this to work for you, you must enrol also for the course labs (in KOS). Otherwise you will not appear in the upload system BRUTE and will not receive invitations to the BBB meeting rooms for lectures/labs.

Details

  • 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: SS21
  • 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

Miscellaneous

courses/xep33sml/start.txt ยท Last modified: 2021/02/10 18:53 by flachbor