**About this course: **Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.
This course is the first in a sequence of three. It describes the two basic PGM representations: Bayesian Networks, which rely on a directed graph; and Markov networks, which use an undirected graph. The course discusses both the theoretical properties of these representations as well as their use in practice. The (highly recommended) honors track contains several hands-on assignments on how to represent some real-world problems. The course also presents some important extensions beyond the basic PGM representation, which allow more complex models to be encoded compactly.

This course is really amazing. The lecture is well-organised and lecture material is good. This course covers basic knowledge about representation in Probabilistic Graphical Model. It includes Markov Network, Bayesian Network, Template Model and some other knowledge. The assignments, oh, I have to say, although some quiz in it seems like having bug, are still impressive. I strongly recommend finishing all the programming assignments of this course. Some trick parts of the knowledge taught in the course are covered by the assignments (like template model part, trust me you have to think about the template model part really, really carefully to figure out what it exactly means). Anyway, it worth my payment :-).

If you wanna take this course, buying a textbook is a good choice because there are some extra knowledge which is not covered by this course in the textbook. However, without a textbook you can still continue. I really appreciate Professor Koller for offering such a great, amazing course!