Stanford University
Probabilistic Graphical Models 1: Representation
Stanford University

Probabilistic Graphical Models 1: Representation

This course is part of Probabilistic Graphical Models Specialization

Taught in English

Some content may not be translated

Daphne Koller

Instructor: Daphne Koller

89,992 already enrolled

Course

Gain insight into a topic and learn the fundamentals

4.6

(1,427 reviews)

|

83%

Advanced level
Designed for those already in the industry
66 hours (approximately)
Flexible schedule
Learn at your own pace

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

12 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.6

(1,427 reviews)

|

83%

Advanced level
Designed for those already in the industry
66 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

Placeholder

Build your subject-matter expertise

This course is part of the Probabilistic Graphical Models Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
Placeholder
Placeholder

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

Placeholder

There are 7 modules in this course

This module provides an overall introduction to probabilistic graphical models, and defines a few of the key concepts that will be used later in the course.

What's included

4 videos1 quiz

In this module, we define the Bayesian network representation and its semantics. We also analyze the relationship between the graph structure and the independence properties of a distribution represented over that graph. Finally, we give some practical tips on how to model a real-world situation as a Bayesian network.

What's included

15 videos6 readings3 quizzes1 programming assignment

In many cases, we need to model distributions that have a recurring structure. In this module, we describe representations for two such situations. One is temporal scenarios, where we want to model a probabilistic structure that holds constant over time; here, we use Hidden Markov Models, or, more generally, Dynamic Bayesian Networks. The other is aimed at scenarios that involve multiple similar entities, each of whose properties is governed by a similar model; here, we use Plate Models.

What's included

4 videos1 quiz

A table-based representation of a CPD in a Bayesian network has a size that grows exponentially in the number of parents. There are a variety of other form of CPD that exploit some type of structure in the dependency model to allow for a much more compact representation. Here we describe a number of the ones most commonly used in practice.

What's included

4 videos2 quizzes1 programming assignment

In this module, we describe Markov networks (also called Markov random fields): probabilistic graphical models based on an undirected graph representation. We discuss the representation of these models and their semantics. We also analyze the independence properties of distributions encoded by these graphs, and their relationship to the graph structure. We compare these independencies to those encoded by a Bayesian network, giving us some insight on which type of model is more suitable for which scenarios.

What's included

7 videos2 quizzes1 programming assignment

In this module, we discuss the task of decision making under uncertainty. We describe the framework of decision theory, including some aspects of utility functions. We then talk about how decision making scenarios can be encoded as a graphical model called an Influence Diagram, and how such models provide insight both into decision making and the value of information gathering.

What's included

3 videos2 quizzes1 programming assignment

This module provides an overview of graphical model representations and some of the real-world considerations when modeling a scenario as a graphical model. It also includes the course final exam.

What's included

1 video1 quiz

Instructor

Instructor ratings
4.7 (92 ratings)
Daphne Koller
Stanford University
3 Courses94,076 learners

Offered by

Recommended if you're interested in Machine Learning

Why people choose Coursera for their career

Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

Learner reviews

Showing 3 of 1427

4.6

1,427 reviews

  • 5 stars

    74.49%

  • 4 stars

    17.86%

  • 3 stars

    5.25%

  • 2 stars

    1.05%

  • 1 star

    1.33%

AM
4

Reviewed on Nov 2, 2018

HE
4

Reviewed on Feb 15, 2020

BM
4

Reviewed on Jun 27, 2017

New to Machine Learning? Start here.

Placeholder

Open new doors with Coursera Plus

Unlimited access to 7,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription

Advance your career with an online degree

Earn a degree from world-class universities - 100% online

Join over 3,400 global companies that choose Coursera for Business

Upskill your employees to excel in the digital economy

Frequently asked questions