Probabilistic Graphical Models 3 Learning

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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Syllabus

Syllabus - What you will learn from this course
Week 1
Learning: Overview
Review of Machine Learning Concepts from Prof. Andrew Ng’s Machine Learning Class (Optional)
Parameter Estimation in Bayesian Networks
Week 2
Learning Undirected Models
Week 3
Learning BN Structure
Week 4
Learning BNs with Incomplete Data
Week 5
Learning Summary and Final
PGM Wrapup

FAQ

When will I have access to the lectures and assignments?

Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

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.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

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

Reviews

Awesome course... builds intuitive thinking for developing intelligent algorithms...

1) The fórums need better assistance.

2) If we could submit Python code por the homework assignments, that would be much better for me.

Excellent course! Everyone interested in PGM should consider!

Great course, though with the progress of ML/DL, content seems a touch outdated. Would