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 second in a sequence of three. Following the first course, which focused on representation, this course addresses the question of probabilistic inference: how a PGM can be used to answer questions. Even though a PGM generally describes a very high dimensional distribution, its structure is designed so as to allow questions to be answered efficiently. The course presents both exact and approximate algorithms for different types of inference tasks, and discusses where each could best be applied. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of the most commonly used exact and approximate algorithms are implemented and applied to a real-world problem.

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- Week 1 -
**Inference Overview**

This module provides a high-level overview of the main types of inference tasks typically encountered in graphical models: conditional probability queries, and finding the most likely assignment (MAP inference). - Week 1 -
**Variable Elimination**

This module presents the simplest algorithm for exact inference in graphical models: variable elimination. We describe the algorithm, and analyze its complexity in terms of properties of the graph structure. - Week 2 -
**Belief Propagation Algorithms**

This module describes an alternative view of exact inference in graphical models: that of message passing between clusters each of which encodes a factor over a subset of variables. This framework provides a basis for a variety of exact and approximate inferen... - Week 3 -
**MAP Algorithms**

This module describes algorithms for finding the most likely assignment for a distribution encoded as a PGM (a task known as MAP inference). We describe message passing algorithms, which are very similar to the algorithms for computing conditional probabilitie... - Week 4 -
**Sampling Methods**

In this module, we discuss a class of algorithms that uses random sampling to provide approximate answers to conditional probability queries. Most commonly used among these is the class of Markov Chain Monte Carlo (MCMC) algorithms, which includes the simple G... - Week 4 -
**Inference in Temporal Models**

In this brief lesson, we discuss some of the complexities of applying some of the exact or approximate inference algorithms that we learned earlier in this course to dynamic Bayesian networks. - Week 5 -
**Inference Summary**

This module summarizes some of the topics that we covered in this course and discusses tradeoffs between different algorithms. It also includes the course final exam.

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5 / 5

Terminé

I kind of like the teacher. She can always explain complicated things in a simple way, though the notes she writes in the slides are all in free style. Loopy belief propagation and dual decomposition are the best things I've learnt in this course. I've met them before in some papers, but I found it extremely hard to understand then. Now I gain some significant intuition of them and I'm ready to do further exploration. Anyway, I'll keep on learning course 3 to achieve my first little goal in courser.

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4 / 5

Terminé

Unlike other Coursera courses, this specialization covers a lot of conepts accompanied with programming assignments. Since the programming assignments are pre-filled, its a bit tough to understand the style. It would be great if some form of explanation if offered.

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4 / 5

Terminé

great course, though really advanced. would like a bit more examples especially regarding the coding. worth it overally

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5 / 5

Terminé

Perhaps the best introduction to AI/ML - especially for those who think "the future ain't what it used to be"; the mathematical techniques covered by the course form a toolkit which can be easily thought of as "core", i.e. a locus of strength which enables a wide universe of thinking about complex problems (many of which were correctly not thought to be tractable in practice until very recently!)...

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4 / 5

Terminé

Unfortunately, in my opinion, this course is not as well structured as the first course (PGM1: structure). There are some bugs/issues with the PAs code that should have been fixed and the course material could focus a bit more on the case of continuous random variables (which are almost ignored throughout the course). It is still a great and totally worth it course, though. Highly recommended for machine learning post-graduate students.

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4 / 5

Terminé

Pretty good course, albeit very dense compared to the first one (which was certainly not trivial). I would give it 5 stars just based on the content, but the programming assignments don't work without significant extra effort. I completed the honors track for the first course, but gave up after spending 4 hours trying to fix HW bugs that were reported 8 months ago. Would have also been nice to have more practical examples to work on. Some of the material is very theoretical, and I find it hard to build intuitions without applying the algorithms in practice.

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3 / 5

Terminé

Thumbs up for the course content. However, there are technical problems which no one is attending to. I could not submit my programming assignment, and after consulting every available resource, I was not dignified with an answer. It is a shame how such wonderful learning opportunity can become spoiled by some insignificant technical detail.By my opinion, the course should not be divided into 3 courses. Many technicalities were done sloppy in the process.

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4 / 5

Terminé

it is a great class. but the presentation of the materials could be better: maybe each unit should start with a review of the key concepts we learned before? maybe a slide on motivation of the work before we dive deep into the math? but again, this is a great class! recommended 100%

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4 / 5

Terminé

Great Course, not five stars just because probabbly it was too much content for the period of time we had the Course. I've got no complaints about the amount of content, but some of concepts were missing and the Programming Assignments were not so well described, sometimes I couldn't understand what to do.

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5 / 5

Terminé

I learned pretty much from this course. It answered my quandaries from the representation course, and as well deepened my understanding of PGM.

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