About this Course

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Shareable Certificate
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Coursera Labs
Includes hands on learning projects.
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Advanced Level
Approx. 66 hours to complete
English

Skills you will gain

  • Algorithms
  • Expectation–Maximization (EM) Algorithm
  • Graphical Model
  • Markov Random Field
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Coursera Labs
Includes hands on learning projects.
Learn more about Coursera Labs External Link
Advanced Level
Approx. 66 hours to complete
English

Instructor

Offered by

Placeholder

Stanford University

Syllabus - What you will learn from this course

Week1
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)
Week2
Week 2
21 hours to complete

Learning Undirected Models

21 hours to complete
3 videos (Total 52 min)
Week3
Week 3
18 hours to complete

Learning BN Structure

18 hours to complete
7 videos (Total 106 min)
Week4
Week 4
22 hours to complete

Learning BNs with Incomplete Data

22 hours to complete
5 videos (Total 83 min)

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About the Probabilistic Graphical Models Specialization

Probabilistic Graphical Models

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