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 third in a sequence of three. Following the first course, which focused on representation, and the second, which focused on inference, this course addresses the question of learning: how a PGM can be learned from a data set of examples. The course discusses the key problems of parameter estimation in both directed and undirected models, as well as the structure learning task for directed models. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of two commonly used learning algorithms are implemented and applied to a real-world problem.

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Course 3 of 3 in the

Probabilistic Graphical Models Specialization

Advanced Level

Approx. 66 hours to complete

English

Skills you will gain

Algorithms
Expectation–Maximization (EM) Algorithm
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.

Course 3 of 3 in the

Probabilistic Graphical Models Specialization

Advanced Level

Approx. 66 hours to complete

English

Instructor

Instructor rating

4.55/5 (9 Ratings)

Daphne Koller

Professor

School of Engineering

89,152 Learners

3 Courses

Offered by

Stanford University

Syllabus - What you will learn from this course

Week1

Week 1

16 minutes to complete

Learning: Overview

16 minutes to complete

1 video (Total 16 min)

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)

2 hours to complete

Parameter Estimation in Bayesian Networks

2 hours to complete

5 videos (Total 77 min)

Week2

Week 2

21 hours to complete

Learning Undirected Models

21 hours to complete

3 videos (Total 52 min)

Week3

Week 3

18 hours to complete

Learning BN Structure

This module discusses the problem of learning the structure of Bayesian networks. We first discuss how this problem can be formulated as an optimization problem over a space of graph structures, and what are good ways to score different structures so as to trade off fit to data and model complexity. We then talk about how the optimization problem can be solved: exactly in a few cases, approximately in most others.

18 hours to complete

7 videos (Total 106 min)

Week4

Week 4

22 hours to complete

Learning BNs with Incomplete Data

22 hours to complete

5 videos (Total 83 min)

Reviews

4.6

53 reviews

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2 stars
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1 star
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TOP REVIEWS FROM PROBABILISTIC GRAPHICAL MODELS 3: LEARNING

by USSep 5, 2020

Amazing! This is the first specialization that I have finished and it feels amazing! Daphne was amazing!

by AKNov 8, 2017

Awesome course... builds intuitive thinking for developing intelligent algorithms...

by LCAug 27, 2018

Great course, though with the progress of ML/DL, content seems a touch outdated. Would

by SJApr 19, 2017

Tougher course than the 2 preceding ones, but definitely worthwhile.

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
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

More questions? Visit the Learner Help Center.

Probabilistic Graphical Models 3: Learning

About this Course

Skills you will gain