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Probabilistic Graphical Models 3: Learning に戻る

スタンフォード大学(Stanford University) による Probabilistic Graphical Models 3: Learning の受講者のレビューおよびフィードバック

4.6
292件の評価
51件のレビュー

コースについて

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....

人気のレビュー

SP

2020年10月11日

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.

LL

2018年1月29日

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.

フィルター:

Probabilistic Graphical Models 3: Learning: 26 - 50 / 50 レビュー

by rishi c

2020年5月7日

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

by Una S

2020年9月6日

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

by Liu Y

2018年8月27日

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

by Anil K

2017年11月9日

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

by ivan v

2017年10月20日

Excellent course. Programming assignments are excellent and extremely instructive.

by Khalil M

2017年4月3日

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

by Stian F J

2017年4月20日

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

by 张文博

2017年3月6日

Excellent course! Everyone interested in PGM should consider!

by Sriram P

2017年6月24日

Had a wonderful Experience, Thank you Daphne Ma'am

by Wenjun W

2017年7月30日

Very challenging and fulfilling class!

by 郭玮

2019年11月12日

Great course, very helpful.

by Yang P

2017年6月20日

Very useful course.

by Alexander K

2017年6月4日

Thank You for all.

by Alireza N

2017年1月12日

Excellent!

by Allan J

2017年3月4日

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

2021年3月3日

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

2017年5月21日

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 LV

2018年6月5日

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

2018年7月7日

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

by Luiz C

2018年8月27日

Great course, though with the progress of ML/DL, content seems a touch outdated.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

by AlexanderV

2021年5月13日

Octave programming assignments, instead of Python

by Paul-Andre R

2021年3月19日

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.

by Siwei Y

2017年2月3日

上课的方式过于抽象艰涩, 即便是谈到实际应用例子也是说得云里雾里的. 而且练习跟课里的内容联系不紧密. 这样导致为了通过练习和最后考试, 很多时候 是利用考试策略或者说穷举排除法. 也就是说其实学生没有真正理解课里的概念. 还是那句话,我相信有人能上得比这个好的多. 有人说上此课需要有一定的背景知识,我想说, 那些有一定背景知识的人也不需要上这个课了. 最后真心感谢牛姐介绍了如此多有意思的东西. 感谢她们团队设计的PA . 这个东西确实不容易.

by mgbacher

2021年3月6日

Although the contents and the way Daphne explains the subject are of top quality, the rest of the specialization leaves a deep frustrating feeling. There is no TA present at all during the courses. Since the quizzes and final exams are dubious, sometimes pedants, and written in an extremely confusing fashion, you end up guessing instead of applying what you have learned. The book is essential, not a recommendation, and needs to be carefully read. Moreover, PAs are not clear, sometimes as a chunk of some other larger PA, the code is full of bugs so that sometimes only Matlab (nop, no Python, sorry) works, and sometimes you have to look into the net to see how to correct bugs to be able to submit the code. Again, no TAs, so all fall into the blogs, and forums. This is very, very frustrating. The final exam can be re-taken in intervals of 24 Hs. So if you happen to start three days before the end of the course you may find yourself paying again to just take the final exam. An interval of 4-8 Hs may be sufficient since the lectures are not so long. Since the confusion in the questions of the final exam is huge, almost sure you will have to re-try the exams at least twice. I think the intention was to make the course intensive and difficult, but the way it was chosen, transforms the course into an epic failure. Summary: if you are looking to gather knowledge, stay away from this specialization. There are plenty of free courses with deep explanations, addressing modern techniques, and correct code PAs, under the same limitations: you are alone, no TAs or colleagues to be asked. (e.g., https://www.cs.cmu.edu/~epxing/Class/10708-20/lectures.html, by March 2021). If it happens that the specialization is paid by the company you are working for, then go ahead, but keep in mind that you are alone. If it is no, do not waste time and money. I have finished the specialization with honors (almost 100%). Still, I am deeply disappointed. The two stars are given due to Daphne.

by Jiaxing L

2017年2月11日

Managed to be get worse and worse