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

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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.

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.

Probabilistic Graphical Models 3: Learning

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Probabilistic Graphical Models 3: Learning

This course is part of Probabilistic Graphical Models Specialization

Instructor: Daphne Koller

22,475 already enrolled

8 modules

Gain insight into a topic and learn the fundamentals.

304 reviews

Advanced level

Designed for those already in the industry

7 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

8 modules

Gain insight into a topic and learn the fundamentals.

304 reviews

Advanced level

Designed for those already in the industry

7 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

8 assignments

Taught in English

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This course is part of the Probabilistic Graphical Models Specialization

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There are 8 modules in this course

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.

Module details

This module presents some of the learning tasks for probabilistic graphical models that we will tackle in this course.

What's included

1 video

This module contains some basic concepts from the general framework of machine learning, taken from Professor Andrew Ng's Stanford class offered on Coursera. Many of these concepts are highly relevant to the problems we'll tackle in this course.

What's included

6 videos

This module discusses the simples and most basic of the learning problems in probabilistic graphical models: that of parameter estimation in a Bayesian network. We discuss maximum likelihood estimation, and the issues with it. We then discuss Bayesian estimation and how it can ameliorate these problems.

What's included

5 videos2 assignments

In this module, we discuss the parameter estimation problem for Markov networks - undirected graphical models. This task is considerably more complex, both conceptually and computationally, than parameter estimation for Bayesian networks, due to the issues presented by the global partition function.

What's included

3 videos1 assignment1 programming assignment

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.

What's included

7 videos2 assignments1 programming assignment

7 videosTotal 106 minutes

Structure Learning Overview6 minutes
Likelihood Scores17 minutes
BIC and Asymptotic Consistency11 minutes
Bayesian Scores21 minutes
Learning Tree Structured Networks12 minutes
Learning General Graphs: Heuristic Search24 minutes
Learning General Graphs: Search and Decomposability16 minutes

2 assignmentsTotal 60 minutes

Structure Scores30 minutes
Tree Learning and Hill Climbing30 minutes

1 programming assignmentTotal 900 minutes

Learning Tree-structured Networks900 minutes

In this module, we discuss the problem of learning models in cases where some of the variables in some of the data cases are not fully observed. We discuss why this situation is considerably more complex than the fully observable case. We then present the Expectation Maximization (EM) algorithm, which is used in a wide variety of problems.

What's included

5 videos2 assignments1 programming assignment

5 videosTotal 83 minutes

Learning With Incomplete Data - Overview22 minutes
Expectation Maximization - Intro16 minutes
Analysis of EM Algorithm12 minutes
EM in Practice11 minutes
Latent Variables22 minutes

2 assignmentsTotal 60 minutes

Learning with Incomplete Data30 minutes
Expectation Maximization30 minutes

1 programming assignmentTotal 1,200 minutes

Learning with Incomplete Data1,200 minutes

This module summarizes some of the issues that arise when learning probabilistic graphical models from data. It also contains the course final.

What's included

1 video1 assignment

This module contains an overview of PGM methods as a whole, discussing some of the real-world tradeoffs when using this framework in practice. It refers to topics from all three of the PGM courses.