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Probabilistic Graphical Models 3: Learning(으)로 돌아가기

Probabilistic Graphical Models 3: Learning, 스탠퍼드 대학교

4.6
(204개의 평가)

About this 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....

최상위 리뷰

대학: LL

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.

대학: ZZ

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.

필터링 기준:

31개의 리뷰

대학: Akshaya Thippur

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.

대학: Lik Ming Cheong

Feb 23, 2019

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

대학: Shi Yihui

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.

대학: Antônio Horta Ribeiro

Nov 06, 2018

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

대학: Luis

Aug 28, 2018

Great course, though with the progress of ML/DL, content seems a touch outdated.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

대학: Liu Yang

Aug 27, 2018

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

대학: Musalula Sinkala

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.

대학: Michel Speiser

Jul 14, 2018

Good course, but the material really needs a refresh!

대학: Gorazd Hribar Rajterič

Jul 07, 2018

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

대학: Vincent Li

Jun 05, 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.