About this Course

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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.
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)

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About the Probabilistic Graphical Models Specialization

Probabilistic Graphical Models

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