This course will introduce the basic concepts of probabilistic graphical models. Graphical Models are a unified framework that allow to express complex probability distributions in a compact way. Many machine learning applications are tackled by the use of these models, in this course we will highlight the possibilities with computer vision applications.
The main goal of the class is to understand the concepts behind graphical models and to give hands-on knowledge such that one is able to design models for computer vision applications but also in other domains. Therefore the lecture is roughly divided in two parts: learning about graphical models and seeing them in action.
In the first part of the lecture we will discuss the basics of solving these models, eg. for special kinds of graphs where efficient exact inference is possible and approimate methods for the general case. In the second part we will then discuss prominent applications for both low- and high-level computer vision problems. Some examples are statistical models of images (eg denoising), body pose estimation, person tracking, object detection and semantic image segmentation.
The exercises will be a mix of theoretical and practical assignments.
Lecture start: Wednesday, 04.11.
Time and Location:
Lecture: Wednesday 2 pm - 4 pm
Exercise: Friday 8 am - 10 am
VL 2 + Ü 2
Mailing List: Send an email with your matriculation number and full name to abhattac[at]mpi-inf.mpg.de with [pgm-subscribe] in the subject. Exercises can be completed in groups of 2-3 students, please mention your group in your registration email.
Exam: February 11 and March 25
Registration: send an email to abhattac[at]mpi-inf.mpg.de with [pgm exam registration] in the subject
Lecturer(s): Prof. Dr. Bernt Schiele
zoom link for the first lecture:
Topic: PGM Lecture No1
Meeting ID: 940 6649 4694
zoom link for the first exercise:
Meeting ID: 971 4500 7820
- Discrete Graphical Models - An Optimization Perspective, Bogdan Savchynskyy, preprint, online available at Textbook website
- Probabilistic Graphical Models: Principles and Techniques, Daphne Koller and Nir Friedman, MIT Press, 2009 (careful: 1300 pages)
- Bayesian Reasoning and Machine Learning, David Barber, Cambridge University Press, online available at Textbook website (thanks David)
- Pattern Recognition and Machine Learning, Chris Bishop, Springer, 2006