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學生對 斯坦福大学 提供的 Probabilistic Graphical Models 1: Representation 的評價和反饋

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1,356 個評分
302 條評論

課程概述

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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ST
2017年7月12日

Prof. Koller did a great job communicating difficult material in an accessible manner. Thanks to her for starting Coursera and offering this advanced course so that we can all learn...Kudos!!

CM
2017年10月22日

The course was deep, and well-taught. This is not a spoon-feeding course like some others. The only downside were some "mechanical" problems (e.g. code submission didn't work for me).

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51 - Probabilistic Graphical Models 1: Representation 的 75 個評論(共 295 個)

創建者 Ryan D

2017年6月21日

Quiz and Video Lecture content was good. Would have preferred different format for programming assignments. The 30 minute life time of programming assignment submission tokens was pretty inconvenient. Overall great course. Definitely more challenging than the Machine Learning course material.

創建者 Jorge P

2017年2月26日

Brilliant course, extremely challenging. Prof. Koller does a great job explaining the concepts and uses up-to-date and useful examples. The quizzes are the hardest I've faced in Coursera, this course is no joke, it will take time, effort and taking notes to get through it.

創建者 roi s

2017年10月29日

I really like how Dafna is teaching the course, very clear!

It will be nice if their could be a following course that will show new frameworks and code that implements PGMs. Like the courses of deep learning where Andrew Ng is focusing mostly on the practical side.

創建者 Anurag P

2018年1月8日

The course is quite hard, however it becomes easier if you follow the book along with course. 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.

創建者 Yuxuan X

2017年8月7日

Awsome course for Information/Knowledge Engineering. Although not necessary to finish all the honor assignments, it is highly recommended to implement them. Not only for comprehension, but also practice. You can actually apply them on your career or research.

創建者 Minh N

2017年3月1日

Quite a steep learning curve. Definitely not for those without prior experience in machine learning, or statistics in general. Also, I would much appreciate it if more test cases were provided in the programming assignments to help with debugging.

創建者 Christophe K

2016年10月22日

Very challenging course, but hey, if you are here, you are looking for that!

Lots of knowledge to absorb, but that leads you to a deep understanding on Probability Graphs properties.

I've learnt a lot and I really enjoyed taking this course.

創建者 Maxim V

2020年4月29日

Basic but absolutely necessary knowledge (representation). Quizzes were surprisingly easy. The best (and in my opinion absolutely necessary) part are the honor assignments, they make the course not just a little but many times better.

創建者 José A R

2018年9月13日

Excellent course. Very well explained with precise detail and practical material to consolidate knowledge.

This was my first approach to PGM and end it fascinated. Will look to learn more from this subject.

Thank you very much Daphne!!

創建者 Chatard J

2016年11月25日

Une méthode pédagogique sans faille. Des contrôles et des exercices qui permettent d'approfondir ce qu'on apprend et de faire le point en permanence. Un merveilleux voyage dans le monde des Modèles Graphiques Probabilistes.

創建者 Justin C

2016年10月23日

This was a fantastic introduction to PGM for a non-expert. It is well paced for an online course and the assignments provide enough depth to hone your knowledge and skills within the 5 week timeframe. Highly recommended.

創建者 KE Z

2017年11月23日

All Programming Assignments are challenging (Bayesian net, Markov net/CRF, and decision making), but very essential to help understand how PGM works. I definitely will enroll the second course in this specialization.

創建者 Alexey K

2017年11月17日

Thank you! It's simply incredible exercise for brain! :-) The best ever course here, which teaches one to really think and model, rather than merely click to choose most plausible answer ( like other courses do )

創建者 Ofelia P R P

2017年12月11日

Curso muy completo que da conocimiento realmente avanzado sobre modelos gráficos probabilísticos. Aviso, la especialización es complicada para los que no somos expertos del tema!

創建者 Jorge C

2017年9月17日

Sugerencia: Algunos de los ejemplos numéricos presentados en el curso podrían ir acompañados de alguna expresión matemática intermedia que facilite la comprensión de los mismos.

創建者 Christopher M P

2020年1月16日

Simply excellent. A wonderful course to begin the representation of PGM. Be advised.... this can get quite advanced. It's all about that Bayes, 'bout that Bayes.... no trouble.

創建者 Christopher B

2017年7月17日

learned a lot. lectures were easy to follow and the textbook was able to more fully explain things when I needed it. looking forward to the next course in the series.

創建者 Anthony L

2019年7月20日

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.

創建者 ChrisLJ

2020年3月25日

really great course! very clear and logical structure. I completed a graphical models course as part of my master's degree, and this really helped to consolidate it

創建者 Prasid S

2016年12月7日

Very well designed. There were areas here I struggled with the technical details and had to read up a lot to understand. The assignments are very well designed.

創建者 Al F

2018年3月19日

Excellent Course. Very Deep Material. I purchased the Text Book to allow for a deeper understanding and it made the course so much easier. Highly recommended

創建者 Vivek G

2019年4月27日

Great course. some programming assignments are tough (not too nicely worded and automatic grader can be a bit annoying) but all in all, great course

創建者 Sureerat R

2018年3月2日

This subject covered in this course is very helpful for me who interested in inference methods, machine learning, computer vision, and optimization.

創建者 Angel G G

2019年12月12日

Great course, I miss some programming assignments (I didn't do the "honors"), but the quizzes are already good to test your general understanding.

創建者 Ayush T

2019年8月23日

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.