CS179: Introduction to Graphical Models
Lecture: M/W/F 3pm-3:50pm, SSL 290
Weekly review session: W 6-6:50pm, DBH1600
Instructor: Prof. Alex Ihler (firstname.lastname@example.org), Office Bren Hall 4066
Teaching Assistant: Qi Lou
Reader: Shu Kong
Introduction to graphical models
Graphical models are a powerful framework for efficiently representing and reasoning about large systems. Common examples include solving constraint satisfaction and constraint optimization problems, learning and reasoning about high-dimensional, multivariate probability models, and structured prediction tasks. Graphical models are used in a broad spectrum of scientific fields, including computer vision, natural language processing, computational biology, communications and information theory, physics, and more. In this course we will study the common representations of graphical models, including Bayesian networks and Markov random fields; methods for and the complexity of exact reasoning in these models; computationally efficient algorithms for approximate reasoning; and techniques for learning models from data.
We will assume basic familiarity with the concepts of probability. Some programming will be required; we will primarily use Python.
Textbook and Reading
There is no required textbook for the class. However, some useful books on the subject for supplementary reading, on hold at the science library, include:
and available online,
Discussion on Piazza
I prefer that students ask questions within a discussion forum so that everyone (staff and other students) can see the questions, participate in answering them, and learn from each other. I also expect students to read the posts regularly to keep current on the class issues. Please avoid duplicate posts: before posting, first check whether another student has already posted on that topic. When you do post a question, choose a clear and descriptive title. I currently prefer to use Piazza to manage student discussions and questions. Our class link is: http://piazza.com/uci/fall2015/cs179/home.
I encourage you to collaborate in the sense of discussing the course material and ideas with your fellow students. However, it is an important part of the learning process to do your work yourself. To this end, do not show your homework solutions or code to your fellow students, or examine another students solutions or code. Similarly, do not post solution code on Piazza (unless privately to myself and the TA). An excellent guide is the ICS-33 academic integrity handout.
Syllabus (subject to change)
External resources of interest