Вернуться к Probabilistic Graphical Models 3: Learning

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Оценки: 248

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Рецензии: 37

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....

Jan 30, 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.

Feb 14, 2017

Great course! Very informative course videos and challenging yet rewarding programming assignments. Hope that the mentors can be more helpful in timely responding for questions.

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автор: Akshaya T

•Mar 14, 2019

I was very lost with the different depths of lectures and assignments in this part of the course. I felt that some places were super involved mathematically and was trying to understand its implication. In other places it felt like a lot of fluff. I would recommend this only if you have taken the other 2 parts. Also Prof. Koller's lectures are quite confounding and monotonous in these more than the other lectures.

автор: Lik M C

•Feb 23, 2019

A great course! Learned a lot. Especially the assignments are excellent! Thanks a lot.

автор: Antônio H R

•Nov 06, 2018

Bad choice of content. Focus too much on the specific case of table CPDs, missing the big picture.

автор: Dat N

•Nov 14, 2019

The course really helps me understand a lot of things about learning graphical model, from estimating parameters for Bayesian Network, Markov Random Field, CRF, to learning graph structure from data and using EM algorithms to learn when there is missing data. It also gives many guidelines about the process of machine learning in general. I found the programming assignment more challenging than the first 2 parts but at the same time they are very enlightening when all the pieces beautifully fit together. In general, it was a fun, challenging and enlightening learning experience. I want to thank the course instructor and staffs who made this great course possible.

автор: Shi Y

•Jan 20, 2019

I love this course! It's very difficult but worthy. If you are looking for the state-of-the-art AI techniques, PGM doesn't seem to be your best choice. It's some kind of old fashion compared to DL. I learned a lot about the probability theory through all three courses, and I get better understanding with CRF and HMM. Seriously, it's not a course that will improve your skills or guarantee your successful immediately in ML fields, but a course that can shape your thoughts, help you think out of box. So if you don't like the black-box in DL, PGM will offer you another brand new perspective to understand this uncertain world.

автор: Chan-Se-Yeun

•Feb 22, 2018

Yeah! I managed to finish PGM. I feel ready to explore further. PGM 3 is really helpful. Although many details are not fully discussed, some important intuitions are well illustrated, like EM algorithm and its modification in case of incomplete data. Also, the way the teacher teach set an good example for me to learn to demonstrate complicated things in an easy and vivid way. Thank you so much!

автор: Rishi C

•Jun 05, 2018

The course facilitates learning - and reinforces acquired knowledge through the simple principle of honest effort: students are not given all the answers... but they are 'nudged' in the right direction & guided towards fruitful questions; in a way, it's the perfect course!

автор: Musalula S

•Aug 25, 2018

The course is very involved but Daphne makes its palatable. The course open a new world of new possibilities where one can apply PGMs to get concrete understanding of relationships between events and phenomena in any discipline; from social sciences to natural sciences.

автор: Joey W

•Jan 10, 2020

Great course, very dense but informational. Took a lot of time for content to sink in and I had to review it several times, but now I feel confident in my ability to learn structure/parameters in graphical models.

автор: llv23

•Jan 30, 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.

автор: Ziheng

•Feb 14, 2017

Great course! Very informative course videos and challenging yet rewarding programming assignments. Hope that the mentors can be more helpful in timely responding for questions.

автор: Jerry A R

•Jan 29, 2018

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

автор: Liu Y

•Aug 27, 2018

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

автор: Anil K

•Nov 09, 2017

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

автор: ivan v

•Oct 20, 2017

Excellent course. Programming assignments are excellent and extremely instructive.

автор: Khalil M

•Apr 03, 2017

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

автор: Stian F J

•Apr 20, 2017

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

автор: Wenbo Z

•Mar 06, 2017

Excellent course! Everyone interested in PGM should consider!

автор: Sriram P

•Jun 24, 2017

Had a wonderful Experience, Thank you Daphne Ma'am

автор: 王文君

•Jul 30, 2017

Very challenging and fulfilling class!

автор: 郭玮

•Nov 13, 2019

Great course, very helpful.

автор: Yang P

•Jun 20, 2017

Very useful course.

автор: Alexander K

•Jun 04, 2017

Thank You for all.

автор: Alireza N

•Jan 12, 2017

Excellent!

автор: Allan J

•Mar 04, 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.