About this Course

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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
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Start instantly and learn at your own schedule.
Flexible deadlines
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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

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 video
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)
6 videos
Regularization: Cost Function 10m
Evaluating a Hypothesis 7m
Model Selection and Train Validation Test Sets 12m
Diagnosing Bias vs Variance 7m
Regularization and Bias Variance11m
2 hours to complete

Parameter Estimation in Bayesian Networks

2 hours to complete
5 videos (Total 77 min)
5 videos
Maximum Likelihood Estimation for Bayesian Networks15m
Bayesian Estimation15m
Bayesian Prediction13m
Bayesian Estimation for Bayesian Networks17m
2 practice exercises
Learning in Parametric Models30m
Bayesian Priors for BNs30m
Week
2

Week 2

21 hours to complete

Learning Undirected Models

21 hours to complete
3 videos (Total 52 min)
3 videos
Maximum Likelihood for Conditional Random Fields13m
MAP Estimation for MRFs and CRFs9m
1 practice exercise
Parameter Estimation in MNs30m
Week
3

Week 3

18 hours to complete

Learning BN Structure

18 hours to complete
7 videos (Total 106 min)
7 videos
Likelihood Scores16m
BIC and Asymptotic Consistency11m
Bayesian Scores20m
Learning Tree Structured Networks12m
Learning General Graphs: Heuristic Search23m
Learning General Graphs: Search and Decomposability15m
2 practice exercises
Structure Scores30m
Tree Learning and Hill Climbing30m
Week
4

Week 4

22 hours to complete

Learning BNs with Incomplete Data

22 hours to complete
5 videos (Total 83 min)
5 videos
Expectation Maximization - Intro16m
Analysis of EM Algorithm11m
EM in Practice11m
Latent Variables22m
2 practice exercises
Learning with Incomplete Data30m
Expectation Maximization30m

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About the Probabilistic Graphical Models Specialization

Probabilistic Graphical Models

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