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Probabilistic Graphical Models 1

Representation

This course is a part of Probabilistic Graphical Models , a 3-course Specialization series from Coursera.

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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Stanford University

Rating 4.4 based on 196 ratings
Length 6 weeks
Starts Dec 30 (in 3 weeks)
Cost $79
From Stanford University via Coursera
Instructor Daphne Koller
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Science Algorithms Machine Learning

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What people are saying

According to other learners, here's what you need to know

programming assignments in 25 reviews

I found the programming assignments to be quite tough, or put differently, more difficult than any other Coursera course.

Definitely, choose an advanced track with programming assignments.

One aspect I like the most about the course is the programming assignments.

A big disadvantage is Matlab/Octave programming assignments.

Also, programming assignments need to improved, the bugs and known issues mentioned in forum should be incorporated to prevent people from wasting time on setup issues.

This specialization covers a lot of concepts and programming assignments which are very helpful in understanding the concepts clearly.

Although, I wish there is some form of explanation for the programming assignments.

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machine learning in 11 reviews

The course is interesting though hard, but lack the great pedagogy of Andrew Ng in his "Machine Learning Course".

Because PGM is one of the basic theory of machine learning and widespread use.

Previous knowledge on basic probability theory and machine learning is highly recommended.

Definitely more challenging than the Machine Learning course material.

Definitely not for those without prior experience in machine learning, or statistics in general.

Before I took this course I took the Stanford Machine Learning course, which I greatly enjoyed.

A great course, a must for those in the machine learning domain.

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graphical models in 9 reviews

Awesome intro to graphical models, and the exercises really emphasize understanding and proceed at what seems like the appropriate pace.

The first part just introduce you two basic frames of graphical models.

I love the way that the professor led us into the graphical models.

Others have pointed out that while this is an introductory course to Probability Graphical Models, I would say that this is still an advanced course, with lots of prerequisites.

The honors assignments are interesting, which instruct you to implement graphical models from scratch to solve problems in real world using Matlab or Octave.

If you are interested in graphical models, you should take this course.

When delivering in this format, allowances need to be made for the facts that tutorial sessions do not exist and the possibilities for informal Q&A are limited so any gaps become very difficult for students to fill in themselves.Despite the above shortcomings I'm glad I did the course and I would still recommend it to someone interested in graphical models as it does cover the basics well enough to make a decent start.

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prof. koller in 7 reviews

Prof. Koller did a great job communicating difficult material in an accessible manner.

Thank you prof. Koller for this course!

Finally, a highly respect to Prof. Koller who provide the course in such a theoretical depth.

Prof. Koller does a great job explaining the concepts and uses up-to-date and useful examples.

Prof. Koller is knowledgeable and presented the materially logically.

Prof. Koller is an excellent lecturer, yet moves fast, and you'll need to do reading to fill in the gaps.

I also purchased the book written by Prof. Koller and Prof Friedman and I am going to continue my study on this subject.

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daphne koller in 7 reviews

Thanks prof. Daphne Koller for this course and Coursera at all.

Daphne Koller is very brilliant but this makes it hard for people to catch up with her, especially for people whose mother language is not English.

Thanks Daphne Koller, this is really motivating :) This is an amazing course, and taught by an extremely talented and accomplished professor.

Very well instructed by Prof Daphne Koller.

Comprehensive programming assignment with honour content and quizzes help to make yourself very familar with the topics: #bayesiannetwork #gibbssampling #intercasualreasoning #markovproccess #markovchain #OCR Daphne Koller @DaphneKoller , as Coursera co-funder, made her best to show the capabilities of the platform.

Prof Daphne Koller is one of the very few authority on this subject.

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understand what in 5 reviews

The instruction is solid but you still need to reason through a lot on your own, and especially if you choose to complete the Honors programming section (which I highly recommend to prove to yourself that you really understand what you have learned and can apply it), you really need to plan on allocating sufficient number of hours to work through the programming assignments.

The course helps me understand what a probabilistic graphical model is and how and why it works.

And now I am at one point in the course, that is "Flow of Probalistic Influence", where she explains a concept without explaining what is meant with the used underlying notions "flow" and "influence" which makes me difficult to understand what is going on.

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Coursera

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Stanford University

Rating 4.4 based on 196 ratings
Length 6 weeks
Starts Dec 30 (in 3 weeks)
Cost $79
From Stanford University via Coursera
Instructor Daphne Koller
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Science Algorithms Machine Learning

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