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Probabilistic Graphical Models 1: Representation

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 mor

Created by: Daphne Koller

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Overall Score : 90 / 100

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Course Description

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.

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Instructor Details

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Professor Daphne Koller joined the faculty at Stanford University in 1995, where she is now the Rajeev Motwani Professor in the School of Engineering. Her main research interest is in developing and using machine learning and probabilistic methods to model and analyze complex domains. Her current research projects span computational biology, computational medicine, and semantic understanding of the physical world from sensor data. She is the author of over 200 refereed publications, which have appeared in venues that range from Science to numerous conferences and journals in AI and Computer Science. She has given keynote talks at over 10 different major conferences, also spanning a variety of areas. She was awarded the Arthur Samuel Thesis Award in 1994, the Sloan Foundation Faculty Fellowship in 1996, the ONR Young Investigator Award in 1998, the Presidential Early Career Award for Scientists and Engineers (PECASE) in 1999, the IJCAI Computers and Thought Award in 2001, the Cox Medal for excellence in fostering undergraduate research at Stanford in 2003, the MacArthur Foundation Fellowship in 2004, the ACM/Infosys award in 2008, and was elected a member of the National Academy of Engineering in 2011. Daphne Koller is the founder and leader of CURIS, Stanford's summer research experience for undergraduates in computer science - a program that has trained more than 500 students in its decade of existence. In 2010, she initiated and piloted, in her Stanford class, the online ed

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Reviews

4.5

233 reviews in total

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By CIST N on 30-Oct-19

Good way to learn Probabilistic Graphical Models in practical

By Pouya E on 13-Oct-19

Well-structured content, engaging programming assignments in honors track.

By hyesung J on 10-Oct-19

So difficult. But interesting

By Meysam G on 12-Sep-19

I had actually read the David Barber book before I took this course. The course provides a deep insight to the PGMs which is necessary if one wants to utilize it in real applications or as in my case in research works. Moreover, the language of the instructor is comfortably plain, especially when it comes to explaining somewhat complicated concepts. In general, it is highly recommended.

By Ayush T on 23-Aug-19

This course is really good. It is well organized and taught in the best way which really helped me to implement similar ideas for my projects.

By Parag H S on 14-Aug-19

Learn the basic things in probability theory

By Mike P on 30-Jul-19

An excellent course, Daphne is one of the top people to be teaching this topic and does an excellent job in presentation.

By Anthony L on 20-Jul-19

Some parts are challenging enough in the PAs, if you are familiar with Matlab this course is a great opportunity to get familiar with PGMs and learn to handle these.

By Harshdeep S on 19-Jul-19

Excellent blend of maths & intuition.

By Nijesh on 18-Jul-19

Thanks for offering

By Soumyadipta D on 16-Jul-19

lectures are too fast otherwise great

By Jui-wen L on 21-Jun-19

Easy to follow and very informative.

Showing 12 of 233 reviews