About this Course
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Advanced Level

Approx. 30 hours to complete


Subtitles: English

Skills you will gain

Bayesian NetworkGraphical ModelMarkov Random Field

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Advanced Level

Approx. 30 hours to complete


Subtitles: English

Syllabus - What you will learn from this course

1 hour to complete

Introduction and Overview

4 videos (Total 35 min), 1 quiz
4 videos
Overview and Motivation19m
1 practice exercise
Basic Definitions8m
10 hours to complete

Bayesian Network (Directed Models)

15 videos (Total 190 min), 6 readings, 4 quizzes
15 videos
Reasoning Patterns9m
Flow of Probabilistic Influence14m
Conditional Independence12m
Independencies in Bayesian Networks18m
Naive Bayes9m
Application - Medical Diagnosis9m
Knowledge Engineering Example - SAMIAM14m
Basic Operations 13m
Moving Data Around 16m
Computing On Data 13m
Plotting Data 9m
Control Statements: for, while, if statements 12m
Vectorization 13m
Working on and Submitting Programming Exercises 3m
6 readings
Setting Up Your Programming Assignment Environment10m
Installing Octave/MATLAB on Windows10m
Installing Octave/MATLAB on Mac OS X (10.10 Yosemite and 10.9 Mavericks)10m
Installing Octave/MATLAB on Mac OS X (10.8 Mountain Lion and Earlier)10m
Installing Octave/MATLAB on GNU/Linux10m
More Octave/MATLAB resources10m
3 practice exercises
Bayesian Network Fundamentals6m
Bayesian Network Independencies10m
Octave/Matlab installation2m
1 hour to complete

Template Models for Bayesian Networks

4 videos (Total 66 min), 1 quiz
4 videos
Temporal Models - DBNs23m
Temporal Models - HMMs12m
Plate Models20m
1 practice exercise
Template Models20m
11 hours to complete

Structured CPDs for Bayesian Networks

4 videos (Total 49 min), 3 quizzes
4 videos
Tree-Structured CPDs14m
Independence of Causal Influence13m
Continuous Variables13m
2 practice exercises
Structured CPDs8m
BNs for Genetic Inheritance PA Quiz22m
17 hours to complete

Markov Networks (Undirected Models)

7 videos (Total 106 min), 3 quizzes
7 videos
General Gibbs Distribution15m
Conditional Random Fields22m
Independencies in Markov Networks4m
I-maps and perfect maps20m
Log-Linear Models22m
Shared Features in Log-Linear Models8m
2 practice exercises
Markov Networks8m
Independencies Revisited6m
21 hours to complete

Decision Making

3 videos (Total 61 min), 3 quizzes
3 videos
Utility Functions18m
Value of Perfect Information17m
2 practice exercises
Decision Theory8m
Decision Making PA Quiz18m
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Top reviews from Probabilistic Graphical Models 1: Representation

By STJul 13th 2017

Prof. Koller did a great job communicating difficult material in an accessible manner. Thanks to her for starting Coursera and offering this advanced course so that we can all learn...Kudos!!

By CMOct 23rd 2017

The course was deep, and well-taught. This is not a spoon-feeding course like some others. The only downside were some "mechanical" problems (e.g. code submission didn't work for me).



Daphne Koller

School of Engineering

About Stanford University

The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States....

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

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

  • Apply the basic process of representing a scenario as a Bayesian network or a Markov network

    Analyze the independence properties implied by a PGM, and determine whether they are a good match for your distribution

    Decide which family of PGMs is more appropriate for your task

    Utilize extra structure in the local distribution for a Bayesian network to allow for a more compact representation, including tree-structured CPDs, logistic CPDs, and linear Gaussian CPDs

    Represent a Markov network in terms of features, via a log-linear model

    Encode temporal models as a Hidden Markov Model (HMM) or as a Dynamic Bayesian Network (DBN)

    Encode domains with repeating structure via a plate model

    Represent a decision making problem as an influence diagram, and be able to use that model to compute optimal decision strategies and information gathering strategies

    Honors track learners will be able to apply these ideas for complex, real-world problems

More questions? Visit the Learner Help Center.