PGM

What are Probabilistic Graphical Models?

Uncertainty is unavoidable in real-world applications: we can almost never predict with certainty what will happen in the future, and even in the present and the past, many important aspects of the world are not observed with certainty. Probability theory gives us the basic foundation to model our beliefs about the different possible states of the world, and to update these beliefs as new evidence is obtained. These beliefs can be combined with individual preferences to help guide our actions, and even in selecting which observations to make. While probability theory has existed since the 17th century, our ability to use it effectively on large problems involving many inter-related variables is fairly recent, and is due largely to the development of a framework known as Probabilistic Graphical Models (PGMs). This framework, which spans methods such as Bayesian networks and Markov random fields, uses ideas from discrete data structures in computer science to efficiently encode and manipulate probability distributions over high-dimensional spaces, often involving hundreds or even many thousands of variables. These methods have been used in an enormous range of application domains, which include: web search, medical and fault diagnosis, image understanding, reconstruction of biological networks, speech recognition, natural language processing, decoding of messages sent over a noisy communication channel, robot navigation, and many more. The PGM framework provides an essential tool for anyone who wants to learn how to reason coherently from limited and noisy observations.

About The Course

In this class, you will learn the basics of the PGM representation and how to construct them, using both human knowledge and machine learning techniques; you will also learn algorithms for using a PGM to reach conclusions about the world from limited and noisy evidence, and for making good decisions under uncertainty. The class covers both the theoretical underpinnings of the PGM framework and practical skills needed to apply these techniques to new problems. Topics include: (i) The Bayesian network and Markov network representation, including extensions for reasoning over domains that change over time and over domains with a variable number of entities; (ii) reasoning and inference methods, including exact inference (variable elimination, clique trees) and approximate inference (belief propagation message passing, Markov chain Monte Carlo methods); (iii) learning methods for both parameters and structure in a PGM; (iv) using a PGM for decision making under uncertainty. The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply PGM methods to computer vision, text understanding, medical decision making, speech recognition, and many other areas.

The Instructor

Professor Daphne Koller joined the faculty at Stanford University in 1995, where she is now the Rajeev Motwani Professor in the School of Engineering. Her main research interest is in developing and using machine learning and probabilistic methods to model and analyze complex domains. Her current research projects span computational biology, computational medicine, and semantic understanding of the physical world from sensor data. She is the author of over 200 refereed publications, which have appeared in venues that range from Science to numerous conferences and journals in AI and Computer Science. She has given keynote talks at over 10 different major conferences, also spanning a variety of areas. She was awarded the Arthur Samuel Thesis Award in 1994, the Sloan Foundation Faculty Fellowship in 1996, the ONR Young Investigator Award in 1998, the Presidential Early Career Award for Scientists and Engineers (PECASE) in 1999, the IJCAI Computers and Thought Award in 2001, the Cox Medal for excellence in fostering undergraduate research at Stanford in 2003, the MacArthur Foundation Fellowship in 2004, the ACM/Infosys award in 2008, and was elected a member of the National Academy of Engineering in 2011.

Daphne Koller is the founder and leader of CURIS, Stanford’s summer research experience for undergraduates in computer science – a program that has trained more than 500 students in its decade of existence. In 2010, she initiated and piloted, in her Stanford class, the online education model that has led to the formation of the online courses that are being offered by Stanford to the general public.

Frequently Asked Questions

What are the dates of the class?

The class will start in late January and will last approximately ten weeks.
What are the pre-requisites for the class?

You should be able to program in at least one programming language and have a computer (Windows, Mac or Linux) with internet access. It also helps to have some previous exposure to basic concepts in discrete probability theory (independence, conditional independence, and Bayes’ rule).
What textbook should I buy?

There is no need to buy anything. The lectures will be self-contained and allow the homework to be done without additional reading. For additional depth, you can refer to the best-selling textbook, “Probabilistic Graphical Models: Principles and Techniques” by Daphne and Nir Friedman.
Will students receive a Stanford certificate or grade for completing the course?

No. You will receive a statement of accomplishment from the instructor, which will include information on how well you did and how your performance compared to other online students. Only students admitted to Stanford and enrolled in the regular course can receive credit or a grade, so this is not a Stanford certificate.
Will the text of the lectures be available?

We hope to transcribe the lectures into text to make them more accessible for those not fluent in English. Stay tuned.
Can online students ask questions and/or contact the professors?

Yes, but not directly. Students can submit questions that will be aggregated. Top-ranked questions will be answered by the professor and the teaching staff.
How difficult is the class?

This class does require some abstract thinking and mathematical skills. However, it is designed to require fairly little background, and a motivated student can pick up the background material as the concepts are introduced. We hope that, using our new learning platform, it should be possible for everyone to understand all of the core material.
Will other Stanford resources be available to online students?

No.
Will other free online classes in computer science be offered?

Yes. Stay tuned for more!