About this Course

11,333 recent views

Learner Career Outcomes

43%

started a new career after completing these courses

29%

got a tangible career benefit from this course

17%

got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Advanced Level
Approx. 66 hours to complete
English

Skills you will gain

AlgorithmsExpectation–Maximization (EM) AlgorithmGraphical ModelMarkov Random Field

Learner Career Outcomes

43%

started a new career after completing these courses

29%

got a tangible career benefit from this course

17%

got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Advanced Level
Approx. 66 hours to complete
English

Instructor

Offered by

Placeholder

Stanford University

Syllabus - What you will learn from this course

Week
1

Week 1

16 minutes to complete

Learning: Overview

16 minutes to complete
1 video (Total 16 min)
1 hour to complete

Review of Machine Learning Concepts from Prof. Andrew Ng's Machine Learning Class (Optional)

1 hour to complete
6 videos (Total 59 min)
2 hours to complete

Parameter Estimation in Bayesian Networks

2 hours to complete
5 videos (Total 77 min)
Week
2

Week 2

21 hours to complete

Learning Undirected Models

21 hours to complete
3 videos (Total 52 min)
Week
3

Week 3

18 hours to complete

Learning BN Structure

18 hours to complete
7 videos (Total 106 min)
Week
4

Week 4

22 hours to complete

Learning BNs with Incomplete Data

22 hours to complete
5 videos (Total 83 min)

Reviews

TOP REVIEWS FROM PROBABILISTIC GRAPHICAL MODELS 3: LEARNING

View all reviews

About the Probabilistic Graphical Models Specialization

Probabilistic Graphical Models

Frequently Asked Questions

More questions? Visit the Learner Help Center.