xep33gmm -- Graphical Markov Models

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Markov models on graphs represent a model class widely applied in many areas of computer science, such as computer networks, data security, robotics, pattern recognition and computer vision. The first part of the course covers inference and learning for Markov models on chains and trees. Almost all these tasks including structure learning can be solved by efficient algorithms with polynomial time complexity. The second part of the course addresses graphical models on general graphs. Here on the contrary, practically all inference and learning tasks are NP-complete. The focus is therefore on efficient approximation algorithms.

• Teacher: Boris Flach web-page
• Prerequisites: probability theory and mathematical statistics, graphs and graph algorithms, pattern recognition
• Course format: (2/1)
• Schedule: WS23/24
• 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 (see here for example1, example2, example3), 4CP
• Textbooks and References:
  • Michail I. Schlesinger, Vaclav Hlavač, Ten Lectures on Statistical and Structural Pattern Recognition, chapter 8 [Schlesinger-TLPR2002]
  • Stan Z. Li, Markov Random Field Modeling in Image Analysis [Li-MRFIA2009]
  • Daphne Koller, Nir Friedman, Probabilistic Graphical Models Principles and Techniques [Koller-PGM2009]
  • Christopher M. Bishop, Pattern Recognition and Machine Learning (for additional reading) [Bishop-PRML2006]
  • Andrew Blake, Pushmeet Kohli and Carsten Rother, Markov Random Fields for Vision and Image Processing [Blake-MRFfVIP2011]