About this Course

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Flexible deadlines
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Advanced Level
Approx. 64 hours to complete
English
Subtitles: 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. 64 hours to complete
English
Subtitles: English

Offered by

Stanford University logo

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 Models18m
Bayesian Priors for BNs8m
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 MNs6m
Week
3

Week 3

17 hours to complete

Learning BN Structure

17 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 Scores10m
Tree Learning and Hill Climbing8m
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 Data8m
Expectation Maximization14m

Reviews

TOP REVIEWS FROM PROBABILISTIC GRAPHICAL MODELS 3: LEARNING

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About the Probabilistic Graphical Models Specialization

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems....
Probabilistic Graphical Models

Frequently Asked Questions

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  • Compute the sufficient statistics of a data set that are necessary for learning a PGM from data

    Implement both maximum likelihood and Bayesian parameter estimation for Bayesian networks

    Implement maximum likelihood and MAP parameter estimation for Markov networks

    Formulate a structure learning problem as a combinatorial optimization task over a space of network structure, and evaluate which scoring function is appropriate for a given situation

    Utilize PGM inference algorithms in ways that support more effective parameter estimation for PGMs

    Implement the Expectation Maximization (EM) algorithm for Bayesian networks

    Honors track learners will get hands-on experience in implementing both EM and structure learning for tree-structured networks, and apply them to real-world tasks

  • This Course doesn't carry university credit, but some universities may choose to accept Course Certificates for credit. Check with your institution to learn more. Online Degrees and Mastertrack™ Certificates on Coursera provide the opportunity to earn university credit.

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