Probabilistic Graphical Models 1 Representation

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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Syllabus

Syllabus - What you will learn from this course
Week 1
Introduction and Overview
Bayesian Network (Directed Models)
Week 2
Template Models for Bayesian Networks
Structured CPDs for Bayesian Networks
Week 3
Markov Networks (Undirected Models)
Week 4
Decision Making
Week 5
Knowledge Engineering & Summary

FAQ

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:

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

Reviews

The lecture was a bit too compact and unsystematic. However, if you also do a lot of reading of the textbook, you can learn a lot. Besides, the Quiz and Programming task are of high qualities.

concepts in the videos are well presented. additional readings from the textbook are helpful to cement concepts not explained as thoroughly in the videos

This subject covered in this course is very helpful for me who interested in inference methods, machine learning, computer vision, and optimization.

Great course. some programming assignments are tough (not too nicely worded and automatic grader can be a bit annoying) but all in all, great course