This course will focus on the algorithms for inference and learning with statistical models. We use a framework called probabilistic graphical models which include Bayesian Networks and Markov Random Fields.
We will use examples from traditional vision problems such as image registration and image segmentation, as well as recent problems such as object recognition.
Students will be introduced to probablistic graphical models and will learn how to apply them to problems in image analysis and understanding. The focus will be to study various algorithms for inference and parameter learning.
Will be announced during the lecture.
Performance assessment information (valid until the course unit is held again)