Stanford University
Probabilistic Graphical Models 1: Representation
Stanford University

Probabilistic Graphical Models 1: Representation

This course is part of Probabilistic Graphical Models Specialization

Taught in English

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Daphne Koller

Instructor: Daphne Koller

89,122 already enrolled

Course

Gain insight into a topic and learn the fundamentals

4.6

(1,424 reviews)

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83%

Advanced level
Designed for those already in the industry
66 hours (approximately)
Flexible schedule
Learn at your own pace

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Assessments

12 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.6

(1,424 reviews)

|

83%

Advanced level
Designed for those already in the industry
66 hours (approximately)
Flexible schedule
Learn at your own pace

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This course is part of the Probabilistic Graphical Models Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 7 modules in this course

This module provides an overall introduction to probabilistic graphical models, and defines a few of the key concepts that will be used later in the course.

What's included

4 videos1 quiz

In this module, we define the Bayesian network representation and its semantics. We also analyze the relationship between the graph structure and the independence properties of a distribution represented over that graph. Finally, we give some practical tips on how to model a real-world situation as a Bayesian network.

What's included

15 videos6 readings3 quizzes1 programming assignment

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.

What's included

4 videos1 quiz

A table-based representation of a CPD in a Bayesian network has a size that grows exponentially in the number of parents. There are a variety of other form of CPD that exploit some type of structure in the dependency model to allow for a much more compact representation. Here we describe a number of the ones most commonly used in practice.

What's included

4 videos2 quizzes1 programming assignment

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.

What's included

7 videos2 quizzes1 programming assignment

In this module, we discuss the task of decision making under uncertainty. We describe the framework of decision theory, including some aspects of utility functions. We then talk about how decision making scenarios can be encoded as a graphical model called an Influence Diagram, and how such models provide insight both into decision making and the value of information gathering.

What's included

3 videos2 quizzes1 programming assignment

This module provides an overview of graphical model representations and some of the real-world considerations when modeling a scenario as a graphical model. It also includes the course final exam.

What's included

1 video1 quiz

Instructor

Instructor ratings
4.7 (91 ratings)
Daphne Koller
Stanford University
3 Courses93,138 learners

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