This course is offered by Prof. Daphne Koller from Stanford University and Coursera. The class webpage is here:

 

Daphne Koller is a professor in the Department of Computer Science at Stanford University and a MacArthur Fellowship recipient. Her general research area is artificial intelligence and its application in the biomedical sciences. In 2009 she published a  on Probabilistic Graphical Models together with Nir Friedman.

 

Prerequisites of this course: probability and statistics, machine learning, Matlab or GNU Octave.

 

A probabilistic graphical model (PGM for short) is a probabilistic model for which a graph denotes the conditional independence structure between random variables. It is an advanced topic in machine learning. Since modern machine learning models are nearly all probabilistic and statistically learned, and graph structure is efficient in demonstrating template models, this technique is widely applied. The applications of PGM include medical diagnosis, fault diagnosis, natural language processing, traffic analysis, social network models, message decoding, computer vision (image segmentation, 3D reconstruction, holistic scene analysis), speech recognition, robot localization & mapping, etc.

 

This course will teach fundamental methods in PGM, as well as some real-world applications, like medical diagnosis systems. Also, by accomplishing programming assignments in this course, we are able to use these methods in our work. This is perhaps the most exciting but at the same time, most time-consuming part of this course. The overview of this course will be three parts: representation, inference and learning. Representation introduces graph structures and some basic terms and properties. Inference is how we shall use a trained model to predict results and make decisions. Learning refers to the procedure we build the model and train its parameters, given training data.

 

Graphical models are divided into two categories: Bayesian networks which are denoted by directed graphs and Markov networks which are denoted by undirected graphs. We shall see that these two categories vary a lot. And both of them have many applications.

 

Factor is a basic concept. In a joint distribution, we may break up the distribution into smaller components, each over a smaller space of possibilities. We can then define the overall joint distribution as a product of these components, or factors. And we define the scope of a factor as the set of random variables it is related to. Factor marginalization and factor reduction are almost the same with marginalization and reduction in joint distribution.