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 mor

( 33 Reviews )

Created by: Daphne Koller

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Overall Score : 86 / 100

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 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.

Time: 23h

Instructor Details

Daphne Koller

4.3 Rating
33 Reviews

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

Probabilistic Graphical Models 1: Representation
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- Provider: Coursera
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Free

Probabilistic Graphical Models 2: Inference
4.2 (57 Reviews)

- Provider: Coursera
- Time: 23h

Free

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By Javed A on 28-Aug-18

Great course, though with the progress of ML/DL, content seems a touch outdated.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

By Itai O on 4-Mar-17

Great content. Explores the machine learning techniques with the tightest coupling of statistics with computer science. The Probabilistic Graphical Models series is one of the harder MOOCs to pass. Learners are advised to buy the book and actually read it carefully, preferably in advance of listening to the lectures. The quality of the course is generally high. The discussion is a little muddled at the very end when practical aspects of applying the EM algorithm (for learning when there is missing data) is discussed.

By Jiaxing L on 12-Feb-17

Managed to be get worse and worse

By Ahmed S on 22-Sep-17

Pros:The course covers a highly important relatively large set of topics. If you get the content and managed to pass the quizzes and assignments, you're good to go with PGMs.Cons:The course is quite old, with no support from neither TAs nor instructors. The material isn't updated to match a specialization (even the assignment numbers are old, some test cases aren't updated and the course content and assignments are quite dependent).

By Amine M on 17-Jun-19

Great lectures and terrible assignments. Forum is not helpful at all. In fact, the forum is dead and tutors do not exist. Programming assignments have too many errors which are known within the forum for 4 years but no one is fixing these mistakes. All in all, the topic is highly interesting but the implementation is deficient

By Michel S on 14-Jul-18

Good course, but the material really needs a refresh!

By Diogo P on 15-Nov-17

Just completed the 3 course specialization. If you're interested (and already have some background) in Machine Learning, this specialization is totally worth it. However, if you have trouble solving any of the quizzes or assignments, do not expect to have any kind of support from the TAs. They simply do not respond to any post in the forum, even if it is related with any bug in the programming assignments source code.

By Vincent L on 5-Jun-18

Difficult; requires textbook reading to complete. I could not get samiam to work so I skipped the initial PA. The PA are challenging as well but well worth it if you want to understand how to implement PGMs.

By Niculae I on 21-May-17

This was a very interesting specialization and beside the theoretical information in the videos I liked very much the programming assignments, which helped very much with understanding more deep the matter. The PAs were also very challenging, especially the ones in the learning part (course 3).

By Gorazd H R on 7-Jul-18

A very demanding course with some glitches in lectures and materials. The topic itself is very interesting, educational and useful.

By Dat N on 14-Nov-19

The course really helps me understand a lot of things about learning graphical model, from estimating parameters for Bayesian Network, Markov Random Field, CRF, to learning graph structure from data and using EM algorithms to learn when there is missing data. It also gives many guidelines about the process of machine learning in general. I found the programming assignment more challenging than the first 2 parts but at the same time they are very enlightening when all the pieces beautifully fit together. In general, it was a fun, challenging and enlightening learning experience. I want to thank the course instructor and staffs who made this great course possible.