Stanford University
Probabilistic Graphical Models 3: Learning
Stanford University

Probabilistic Graphical Models 3: Learning

This course is part of Probabilistic Graphical Models Specialization

Taught in English

Some content may not be translated

Daphne Koller

Instructor: Daphne Koller

21,219 already enrolled

Course

Gain insight into a topic and learn the fundamentals

4.6

(298 reviews)

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

8 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.6

(298 reviews)

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 8 modules in this course

This module presents some of the learning tasks for probabilistic graphical models that we will tackle in this course.

What's included

1 video

This module contains some basic concepts from the general framework of machine learning, taken from Professor Andrew Ng's Stanford class offered on Coursera. Many of these concepts are highly relevant to the problems we'll tackle in this course.

What's included

6 videos

This module discusses the simples and most basic of the learning problems in probabilistic graphical models: that of parameter estimation in a Bayesian network. We discuss maximum likelihood estimation, and the issues with it. We then discuss Bayesian estimation and how it can ameliorate these problems.

What's included

5 videos2 quizzes

In this module, we discuss the parameter estimation problem for Markov networks - undirected graphical models. This task is considerably more complex, both conceptually and computationally, than parameter estimation for Bayesian networks, due to the issues presented by the global partition function.

What's included

3 videos1 quiz1 programming assignment

This module discusses the problem of learning the structure of Bayesian networks. We first discuss how this problem can be formulated as an optimization problem over a space of graph structures, and what are good ways to score different structures so as to trade off fit to data and model complexity. We then talk about how the optimization problem can be solved: exactly in a few cases, approximately in most others.

What's included

7 videos2 quizzes1 programming assignment

In this module, we discuss the problem of learning models in cases where some of the variables in some of the data cases are not fully observed. We discuss why this situation is considerably more complex than the fully observable case. We then present the Expectation Maximization (EM) algorithm, which is used in a wide variety of problems.

What's included

5 videos2 quizzes1 programming assignment

This module summarizes some of the issues that arise when learning probabilistic graphical models from data. It also contains the course final.

What's included

1 video1 quiz

This module contains an overview of PGM methods as a whole, discussing some of the real-world tradeoffs when using this framework in practice. It refers to topics from all three of the PGM courses.

What's included

1 video

Instructor

Instructor ratings
4.5 (9 ratings)
Daphne Koller
Stanford University
3 Courses93,441 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 298

4.6

298 reviews

  • 5 stars

    71.14%

  • 4 stars

    19.46%

  • 3 stars

    5.36%

  • 2 stars

    3.02%

  • 1 star

    1%

SJ
5

Reviewed on Apr 19, 2017

JR
4

Reviewed on May 30, 2020

SP
5

Reviewed on Jun 23, 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