This course is part of the Probabilistic Graphical Models Specialization

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Daphne Koller

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Probabilistic Graphical Models SpecializationStanford University

About this Course

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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. 
This course is the first in a sequence of three. It describes the two basic PGM representations: Bayesian Networks, which rely on a directed graph; and Markov networks, which use an undirected graph. The course discusses both the theoretical properties of these representations as well as their use in practice. The (highly recommended) honors track contains several hands-on assignments on how to represent some real-world problems. The course also presents some important extensions beyond the basic PGM representation, which allow more complex models to be encoded compactly.

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Course 1 of 3 in the

Probabilistic Graphical Models Specialization

Advanced Level

Approx. 66 hours to complete

English

Skills you will gain

Bayesian Network
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

Course 1 of 3 in the

Probabilistic Graphical Models Specialization

Advanced Level

Approx. 66 hours to complete

English

Instructor

Instructor rating

4.67/5 (84 Ratings)

Daphne Koller

Professor

School of Engineering

89,103 Learners

3 Courses

Offered by

Stanford University

Syllabus - What you will learn from this course

Content Rating84%(4,003 ratings)

Week1

Week 1

1 hour to complete

Introduction and Overview

1 hour to complete

4 videos (Total 35 min)

12 hours to complete

Bayesian Network (Directed Models)

12 hours to complete

15 videos (Total 190 min), 6 readings, 4 quizzes

15 videos

Semantics & Factorization17mReasoning Patterns9mFlow of Probabilistic Influence14mConditional Independence12mIndependencies in Bayesian Networks18mNaive Bayes9mApplication - Medical Diagnosis9mKnowledge Engineering Example - SAMIAM14mBasic Operations 13mMoving Data Around 16mComputing On Data 13mPlotting Data 9mControl Statements: for, while, if statements 12mVectorization 13mWorking on and Submitting Programming Exercises 3m

6 readings

Setting Up Your Programming Assignment Environment10mInstalling Octave/MATLAB on Windows10mInstalling Octave/MATLAB on Mac OS X (10.10 Yosemite and 10.9 Mavericks)10mInstalling Octave/MATLAB on Mac OS X (10.8 Mountain Lion and Earlier)10mInstalling Octave/MATLAB on GNU/Linux10mMore Octave/MATLAB resources10m

3 practice exercises

Bayesian Network Fundamentals30mBayesian Network Independencies30mOctave/Matlab installation30m

Week2

Week 2

2 hours to complete

Template Models for Bayesian Networks

In many cases, we need to model distributions that have a recurring structure. In this module, we describe representations for two such situations. One is temporal scenarios, where we want to model a probabilistic structure that holds constant over time; here, we use Hidden Markov Models, or, more generally, Dynamic Bayesian Networks. The other is aimed at scenarios that involve multiple similar entities, each of whose properties is governed by a similar model; here, we use Plate Models.

2 hours to complete

4 videos (Total 66 min)

12 hours to complete

Structured CPDs for Bayesian Networks

12 hours to complete

4 videos (Total 49 min)

Week3

Week 3

18 hours to complete

Markov Networks (Undirected Models)

In this module, we describe Markov networks (also called Markov random fields): probabilistic graphical models based on an undirected graph representation. We discuss the representation of these models and their semantics. We also analyze the independence properties of distributions encoded by these graphs, and their relationship to the graph structure. We compare these independencies to those encoded by a Bayesian network, giving us some insight on which type of model is more suitable for which scenarios.

18 hours to complete

7 videos (Total 106 min)

Week4

Week 4

22 hours to complete

Decision Making

22 hours to complete

3 videos (Total 61 min)

Reviews

4.6

313 reviews

5 stars
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TOP REVIEWS FROM PROBABILISTIC GRAPHICAL MODELS 1: REPRESENTATION

by STJul 12, 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 AMNov 2, 2018

Overall very good quality content. PAs are useful but some questions/tests leave too much to interpretation and can be frustrating for students. Audio quality for the classes could also be improved.

by PSDec 7, 2016

Very well designed. There were areas here I struggled with the technical details and had to read up a lot to understand. The assignments are very well designed.

by YTOct 14, 2022

Top notch course! I only wish the explanations for answer choices in the quizzes/exams were more elaborate, as some of them are single sentences that don't really provide justification.

View all reviews

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.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
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.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
Learning Outcomes: By the end of this course, you will be able to
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.

Probabilistic Graphical Models 1: Representation

About this Course

Skills you will gain