Stanford University
Probabilistic Graphical Models 3: Learning
Stanford University

Probabilistic Graphical Models 3: Learning

This course is part of Probabilistic Graphical Models Specialization

Taught in English

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Daphne Koller

Instructor: Daphne Koller

21,149 already enrolled

Course

Gain insight into a topic and learn the fundamentals

4.6

(298 reviews)

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

Details to know

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Assessments

8 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.6

(298 reviews)

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

See how employees at top companies are mastering in-demand skills

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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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  • Gain a foundational understanding of a subject or tool
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There are 8 modules in this course

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 quizzes

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 quiz1 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 quizzes1 programming assignment

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 quizzes1 programming assignment

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 quiz

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.

What's included

1 video

Instructor

Instructor ratings
4.5 (9 ratings)
Daphne Koller
Stanford University
3 Courses93,147 learners

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4.6

298 reviews

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JR
5

Reviewed on Jan 28, 2018

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Reviewed on May 30, 2020

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Reviewed on Jun 23, 2017

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