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 second in a sequence of three. Following the first course, which focused on representation, this course addresses the question of probabilistic inference: how a PGM can be used to answer questions. Even though a PGM generally describes a very high dimensional distribution, its structure is designed so as to allow questions to be answered efficiently. The course presents both exact and approximate algorithms for different types of inference tasks, and discusses where each could best be applied. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of the most commonly used exact and approximate algorithms are implemented and applied to a real-world problem.

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Course 2 of 3 in the

Probabilistic Graphical Models Specialization

Advanced Level

Approx. 38 hours to complete

English

Skills you will gain

Inference
Gibbs Sampling
Markov Chain Monte Carlo (MCMC)
Belief Propagation

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.

Course 2 of 3 in the

Probabilistic Graphical Models Specialization

Advanced Level

Approx. 38 hours to complete

English

Instructor

Instructor rating

4.81/5 (16 Ratings)

Daphne Koller

Professor

School of Engineering

89,086 Learners

3 Courses

Offered by

Stanford University

Syllabus - What you will learn from this course

Week1

Week 1

25 minutes to complete

Inference Overview

25 minutes to complete

2 videos (Total 25 min)

1 hour to complete

Variable Elimination

1 hour to complete

4 videos (Total 56 min)

Week2

Week 2

18 hours to complete

Belief Propagation Algorithms

This module describes an alternative view of exact inference in graphical models: that of message passing between clusters each of which encodes a factor over a subset of variables. This framework provides a basis for a variety of exact and approximate inference algorithms. We focus here on the basic framework and on its instantiation in the exact case of clique tree propagation. An optional lesson describes the loopy belief propagation (LBP) algorithm and its properties.

18 hours to complete

9 videos (Total 150 min)

Week3

Week 3

2 hours to complete

MAP Algorithms

This module describes algorithms for finding the most likely assignment for a distribution encoded as a PGM (a task known as MAP inference). We describe message passing algorithms, which are very similar to the algorithms for computing conditional probabilities, except that we need to also consider how to decode the results to construct a single assignment. In an optional module, we describe a few other algorithms that are able to use very different techniques by exploiting the combinatorial optimization nature of the MAP task.

2 hours to complete

5 videos (Total 74 min)

Week4

Week 4

15 hours to complete

Sampling Methods

15 hours to complete

5 videos (Total 100 min)

1 hour to complete

Inference in Temporal Models

1 hour to complete

1 video (Total 20 min)

Reviews

4.6

74 reviews

5 stars
71.33%
4 stars
21.12%
3 stars
5.23%
2 stars
1.04%
1 star
1.25%

TOP REVIEWS FROM PROBABILISTIC GRAPHICAL MODELS 2: INFERENCE

by KDNov 4, 2018

Great introduction.

It would be great to have more examples included in the lectures and slides.

by JRDec 21, 2017

Great course! Expect to spend significant time reviewing the material.

by JLApr 8, 2018

I would have like to complete the honors assignments, unfortunately, I'm not fluent in Matlab. Otherwise, great course!

by YPMay 28, 2017

I learned pretty much from this course. It answered my quandaries from the representation course, and as well deepened my understanding of PGM.

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 take a given PGM and
Execute the basic steps of a variable elimination or message passing algorithm
Understand how properties of the graph structure influence the complexity of exact inference, and thereby estimate whether exact inference is likely to be feasible
Go through the basic steps of an MCMC algorithm, both Gibbs sampling and Metropolis Hastings
Understand how properties of the PGM influence the efficacy of sampling methods, and thereby estimate whether MCMC algorithms are likely to be effective
Design Metropolis Hastings proposal distributions that are more likely to give good results
Compute a MAP assignment by exact inference
Honors track learners will be able to implement message passing algorithms and MCMC algorithms, and apply them to a real world problem

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Probabilistic Graphical Models 2: Inference

About this Course

Skills you will gain