DD2420 Probabilistic Graphical Models 7.5 credits
Probabilistiska grafiska modeller
This is a relatively advanced course with a flexible set of activities that allow students to chose and between current research applications and theoretical topics to explore. Probabilistic Graphical models are a foundation for understanding many methods of artificial intelligence, machine learning and estimation.
In AI, decisions are made by computers that improve with learning. To do that, programs must perform inference of estimated probabilities given some evidence. That inference can be intractable. The methods learned in this course will allow the student to formulate the AI problem and do both exact and approximate inference.
Machine learning provides algorithms for solving problems by using training data. This course will give insight into how to formulate problems so that machine learning can be used effectively. While end-to-end learning using generic machine learning methods is a successful approach, data is often limited. Building good models can help learn with less data by constraining the learning space.
Bayesian models are at the heart of most estimation methods. Formulation of the these models is the first step in developing an estimation algorithm. The estimation itself is in many cases just inference on the model given some evidence. Approximate inference techniques such as those covered in this course are important in solving many very hard estimation problems in science and engineering.