This course is part of the Probabilistic Graphical Models Specialization

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Probabilistic Graphical Models Specialization

Stanford University

About this Course

4.6

204 ratings

•

31 reviews

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.

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Subtitles: English

AlgorithmsExpectation–Maximization (EM) AlgorithmGraphical ModelMarkov Random Field

Start instantly and learn at your own schedule.

Reset deadlines in accordance to your schedule.

Subtitles: English

Week

1This module presents some of the learning tasks for probabilistic graphical models that we will tackle in this course....

1 video (Total 16 min)

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 (Total 59 min)

Regularization: Cost Function 10m

Evaluating a Hypothesis 7m

Model Selection and Train Validation Test Sets 12m

Diagnosing Bias vs Variance 7m

Regularization and Bias Variance11m

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....

5 videos (Total 77 min), 2 quizzes

Maximum Likelihood Estimation for Bayesian Networks15m

Bayesian Estimation15m

Bayesian Prediction13m

Bayesian Estimation for Bayesian Networks17m

Learning in Parametric Models18m

Bayesian Priors for BNs8m

Week

2In 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....

3 videos (Total 52 min), 2 quizzes

Maximum Likelihood for Conditional Random Fields13m

MAP Estimation for MRFs and CRFs9m

Parameter Estimation in MNs6m

Week

3This 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....

7 videos (Total 106 min), 3 quizzes

Likelihood Scores16m

BIC and Asymptotic Consistency11m

Bayesian Scores20m

Learning Tree Structured Networks12m

Learning General Graphs: Heuristic Search23m

Learning General Graphs: Search and Decomposability15m

Structure Scores10m

Tree Learning and Hill Climbing8m

Week

4In 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....

5 videos (Total 83 min), 3 quizzes

Expectation Maximization - Intro16m

Analysis of EM Algorithm11m

EM in Practice11m

Latent Variables22m

Learning with Incomplete Data8m

Expectation Maximization14m

Week

5This module summarizes some of the issues that arise when learning probabilistic graphical models from data. It also contains the course final....

1 video (Total 20 min), 1 quiz

Learning: Final Exam24m

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 (Total 25 min)

4.6

31 Reviewsstarted a new career after completing these courses

got a tangible career benefit from this course

got a pay increase or promotion

By LL•Jan 30th 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 ZZ•Feb 14th 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.

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....

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....

When will I have access to the lectures and assignments?

Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

What is the refund policy?

Is financial aid available?

Learning Outcomes: By the end of this course, you will be able to

Compute the sufficient statistics of a data set that are necessary for learning a PGM from data

Implement both maximum likelihood and Bayesian parameter estimation for Bayesian networks

Implement maximum likelihood and MAP parameter estimation for Markov networks

Formulate a structure learning problem as a combinatorial optimization task over a space of network structure, and evaluate which scoring function is appropriate for a given situation

Utilize PGM inference algorithms in ways that support more effective parameter estimation for PGMs

Implement the Expectation Maximization (EM) algorithm for Bayesian networks

Honors track learners will get hands-on experience in implementing both EM and structure learning for tree-structured networks, and apply them to real-world tasks

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