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Learner Reviews & Feedback for Probabilistic Graphical Models 3: Learning by Stanford University

4.6
stars
298 ratings

About the Course

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....

Top reviews

SP

Oct 11, 2020

An amazing course! The assignments and quizzes can be insanely difficult espceially towards the conclusion.. Requires textbook reading and relistening to lectures to gather the nuances.

OD

Jan 29, 2018

very good course for PGM learning and concept for machine learning programming. Just some description for quiz of final exam is somehow unclear, which lead to a little bit confusing.

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26 - 50 of 53 Reviews for Probabilistic Graphical Models 3: Learning

By Ruiliang L

Mar 23, 2021

Excellent course. Assignments are challenging but once you figure them out you will have a solid understanding of PGM.

By Jerry R

Jan 29, 2018

Great course! It is pretty difficult - be prepared to study. Leave plenty of time before the final exam.

By rishi c

May 7, 2020

Plz give practical assignments in Python. Matlab is not free and not many and neither myself know Matlab.

By Una S

Sep 6, 2020

Amazing! This is the first specialization that I have finished and it feels amazing! Daphne was amazing!

By Liu Y

Aug 27, 2018

Great course, great assignments I indeed learn much from this course an the whole PGM ialization!

By Anil K

Nov 9, 2017

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

By Ivan V

Oct 20, 2017

Excellent course. Programming assignments are excellent and extremely instructive.

By Khalil M

Apr 3, 2017

Very interesting course. Several methods and algorithms are well-explained.

By Stian F J

Apr 20, 2017

Tougher course than the 2 preceding ones, but definitely worthwhile.

By 张文博

Mar 6, 2017

Excellent course! Everyone interested in PGM should consider!

By Sriram P

Jun 24, 2017

Had a wonderful Experience, Thank you Daphne Ma'am

By Wenjun W

Jul 30, 2017

Very challenging and fulfilling class!

By 郭玮

Nov 12, 2019

Great course, very helpful.

By Hippolyte W

Oct 5, 2022

Definitively worth it !

By Yang P

Jun 20, 2017

Very useful course.

By Alexander K

Jun 4, 2017

Thank You for all.

By Alireza N

Jan 12, 2017

Excellent!

By Allan J

Mar 4, 2017

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 James C

Mar 3, 2021

The lecturer and theoretical aspects of the course are great. The final assessment is challenging but a couple of the questions are ambiguous and imprecise - which was a little frustrating given the quality of the content of the lectures. Honours assignments are now quite dated and contain some excruciating bugs. Overall, worthwhile to take the course, but the assignments (and especially the optional content) could do with revision.

By nicu@ionita.at

May 21, 2017

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 Vincent L

Jun 5, 2018

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 Gorazd H R

Jul 7, 2018

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

By Luiz C

Aug 27, 2018

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

By AlexanderV

May 13, 2021

Octave programming assignments, instead of Python

By Paul-Andre R

Mar 19, 2021

It was a good class. I have been cruising through the 1st, 2nd and this third class of the specialization..... until the last week. The last assignment and the final exam were significantly more challenging for me that the previous ones. I had not budgeted enough time. It is fine to make the class hard..... however, I think it should have been uniformly hard..... not suddenly and unexpectedly harder at the very end, after I have invested many week-ends in this learning.