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 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.

Created by: Stanford University

Taught by: Daphne Koller, Professor
School of Engineering

Basic Info	Course 1 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	4.7 stars Average User Rating 4.7See what learners said

Syllabus

WEEK 1

Introduction and Overview

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.

4 videos

Graded: Basic Definitions

Bayesian Network (Directed Models)

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...

15 videos, 6 readings

Graded: Bayesian Network Fundamentals

Graded: Bayesian Network Independencies

Graded: Octave/Matlab installation

Graded: Simple BN Knowledge Engineering

WEEK 2

Template Models for Bayesian Networks

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,...

4 videos

Graded: Template Models

Structured CPDs for Bayesian Networks

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 repr...

4 videos

Graded: Structured CPDs

Graded: BNs for Genetic Inheritance

Graded: BNs for Genetic Inheritance PA Quiz

WEEK 3

Markov Networks (Undirected Models)

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 prop...

7 videos

Graded: Markov Networks

Graded: Independencies Revisited

Graded: Markov Networks for OCR

WEEK 4

Decision Making

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 ...

3 videos

Graded: Decision Theory

Graded: Decision Making

Graded: Decision Making PA Quiz

WEEK 5

Knowledge Engineering & Summary

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.

1 video

Graded: Representation Final Exam

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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.

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Ratings and Reviews

Rated 4.7 out of 5 of 430 ratings

Had a wonderful learning experience, Thank You Daphne Ma'am.

Quiz and Video Lecture content was good. Would have preferred different format for programming assignments. The 30 minute life time of programming assignment submission tokens was pretty inconvenient. Overall great course. Definitely more challenging than the Machine Learning course material.

I have Actually Earned Three Years of my life (at least) and one possible patent because of this course.

Thank You Daphne Mam. God Bless Everybody Associated with it.

Excellent material presentation

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Probabilistic Graphical Models 1: Representation

Probabilistic Graphical Models 1: Representation

Probabilistic Graphical Models 1: Representation