Probabilistic graphical models are a powerful framework for representing complex domains using probability distributions, with numerous applications in machine learning, computer vision, natural language processing and computational biology. Graphical models bring together graph theory and probability theory, and provide a flexible framework for modeling large collections of random variables with complex interactions.

This course will provide a comprehensive survey of the topic, introducing you to the key formalisms and main techniques used to construct them, make predictions, and support decision-making under uncertainty.

The aim of this course is to help you develop the knowledge and skills necessary to design, implement and apply these models to solve real problems.

Topics Include

• Bayesian networks, undirected graphical models and their temporal extensions
• Exact and approximate inference methods
• Estimation of the parameters and the structure of graphical models.

What You Need to Succeed

• A conferred bachelor’s degree with an undergraduate GPA of 3.0 or better
• Basic probability theory and statistics (e.g., CS109 or STATS116 and STATS217)
• Programming, including algorithm design and analysis (e.g, Foundations in Computer Science Graduate Program)

What You Need To Get Started

Before enrolling in your first graduate course, you must complete an online application.

Don’t wait! While you can only enroll in courses during open enrollment periods, you can complete your online application at any time.

Once you have enrolled in a course, your application will be sent to the department for approval. You will receive an email notifying you of the department's decision after the enrollment period closes. You can also check your application status in your mystanfordconnection account at any time.

Learn more about the graduate application process.

How Much It Will Cost

Learn more about tuition and fees.