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 part of the Probabilistic Graphical Models Specialization
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Probabilistic Graphical Models SpecializationStanford UniversityAbout this Course
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Learner Career Outcomes
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started a new career after completing these courses
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got a tangible career benefit from this course
17%
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Course 3 of 3 in the
Advanced Level
Approx. 66 hours to complete
English
Subtitles: French, Portuguese (European), Russian, English, Spanish
Skills you will gain
AlgorithmsExpectation–Maximization (EM) AlgorithmGraphical ModelMarkov Random Field
Learner Career Outcomes
43%
started a new career after completing these courses
29%
got a tangible career benefit from this course
17%
got a pay increase or promotion
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 3 of 3 in the
Advanced Level
Approx. 66 hours to complete
English
Subtitles: French, Portuguese (European), Russian, English, Spanish
Offered by
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.
Syllabus - What you will learn from this course
16 minutes to complete
Learning: Overview
This module presents some of the learning tasks for probabilistic graphical models that we will tackle in this course.
16 minutes to complete
1 video (Total 16 min)
1 hour to complete
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.
1 hour to complete
6 videos (Total 59 min)
2 hours to complete
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 estimation and how it can ameliorate these problems.
2 hours to complete
5 videos (Total 77 min)
21 hours to complete
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 presented by the global partition function.
21 hours to complete
3 videos (Total 52 min)
18 hours to complete
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 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.
18 hours to complete
7 videos (Total 106 min)
22 hours to complete
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 Expectation Maximization (EM) algorithm, which is used in a wide variety of problems.
22 hours to complete
5 videos (Total 83 min)
Reviews
TOP REVIEWS FROM PROBABILISTIC GRAPHICAL MODELS 3: LEARNING
by SPOct 11, 2020
An amazing course! The assignments and quizzes can be insanely difficult espceially towards the conclusion.. Requires textbook reading and relistening to lectures to gather the nuances.
by LLJan 29, 2018
very good course for PGM learning and concept for machine learning programming. Just some description for quiz of final exam is somehow unclear, which lead to a little bit confusing.
by ZZFeb 13, 2017
Great course! Very informative course videos and challenging yet rewarding programming assignments. Hope that the mentors can be more helpful in timely responding for questions.
by MVApr 29, 2020
Great course, especially the programming assignments. Textbook is pretty much necessary for some quizzes, definitely for the final one.
About the Probabilistic Graphical Models Specialization
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.
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Learning Outcomes: By the end of this course, you will be able to
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