About this Specialization
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100% online courses

Start instantly and learn at your own schedule.

Flexible Schedule

Set and maintain flexible deadlines.

Advanced Level

Approx. 4 months to complete

Suggested 7 hours/week

English

Subtitles: English
User
Learners taking this Specialization are
  • Machine Learning Engineers
  • Data Scientists
  • Research Assistants
  • Researchers
  • Biostatisticians

Skills you will gain

InferenceBayesian NetworkBelief PropagationGraphical Model
User
Learners taking this Specialization are
  • Machine Learning Engineers
  • Data Scientists
  • Research Assistants
  • Researchers
  • Biostatisticians

100% online courses

Start instantly and learn at your own schedule.

Flexible Schedule

Set and maintain flexible deadlines.

Advanced Level

Approx. 4 months to complete

Suggested 7 hours/week

English

Subtitles: English

How the Specialization Works

Take Courses

A Coursera Specialization is a series of courses that helps you master a skill. To begin, enroll in the Specialization directly, or review its courses and choose the one you'd like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. It’s okay to complete just one course — you can pause your learning or end your subscription at any time. Visit your learner dashboard to track your course enrollments and your progress.

Hands-on Project

Every Specialization includes a hands-on project. You'll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you'll need to finish each of the other courses before you can start it.

Earn a Certificate

When you finish every course and complete the hands-on project, you'll earn a Certificate that you can share with prospective employers and your professional network.

how it works

There are 3 Courses in this Specialization

Course1

Probabilistic Graphical Models 1: Representation

4.7
1,097 ratings
243 reviews
Course2

Probabilistic Graphical Models 2: Inference

4.6
370 ratings
56 reviews
Course3

Probabilistic Graphical Models 3: Learning

4.6
223 ratings
32 reviews

Instructor

Avatar

Daphne Koller

Professor
School of Engineering

About Stanford University

The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States....

Frequently Asked Questions

  • Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

  • This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

  • The Specialization has three five-week courses, for a total of fifteen weeks.

  • 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.

    Though, you should be able to program in at least one programming language and have a computer (Windows, Mac or Linux) with internet access (programming assignments will be conducted in Matlab or Octave). It also helps to have some previous exposure to basic concepts in discrete probability theory (independence, conditional independence, and Bayes' rule).

  • For best results, the courses should be taken in order.

  • No.

  • You will be able to take a complex task and understand how it can be encoded as a probabilistic graphical model. You will now know how to implement the core probabilistic inference techniques, how to select the right inference method for the task, and how to use inference to reason. You will also know how to take a data set and use it to learn a model, whether from scratch, or to refine or complete a partially specified model.

More questions? Visit the Learner Help Center.