About 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.

Created by: Stanford University

Taught by: Daphne Koller, Professor
School of Engineering

Basic Info	Course 3 of 3 in the Probabilistic Graphical Models Specialization.
Level	Advanced
Language	English
How To Pass	Pass all graded assignments to complete the course.
User Ratings	4.5 stars Average User Rating 4.5See what learners said

Syllabus

WEEK 1

Learning: Overview

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

1 video

Review of Machine Learning Concepts from Prof. Andrew Ng's Machine Learning Class (Optional)

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.

6 videos

Parameter Estimation in Bayesian Networks

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 estimati...

5 videos

Graded: Learning in Parametric Models

Graded: Bayesian Priors for BNs

WEEK 2

Learning Undirected Models

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 pre...

3 videos

Graded: Parameter Estimation in MNs

Graded: CRF Learning for OCR

WEEK 3

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 tra...

7 videos

Graded: Structure Scores

Graded: Tree Learning and Hill Climbing

Graded: Learning Tree-structured Networks

WEEK 4

Learning BNs with Incomplete Data

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 Expe...

5 videos

Graded: Learning with Incomplete Data

Graded: Expectation Maximization

Graded: Learning with Incomplete Data

WEEK 5

Learning Summary and Final

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

1 video

Graded: Learning: Final Exam

PGM Wrapup

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.

1 video

FAQs

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Coursework

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Stanford University

The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States.

Pricing

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Ratings and Reviews

Rated 4.5 out of 5 of 34 ratings

Very useful course.

Thank You for all.

This was a very interesting specialization and beside the theoretical information in the videos I liked very much the programming assignments, which helped very much with understanding more deep the matter. The PAs were also very challenging, especially the ones in the learning part (course 3).

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

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Probabilistic Graphical Models 3: Learning

Probabilistic Graphical Models 3: Learning

Probabilistic Graphical Models 3: Learning