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學生對 华盛顿大学 提供的 Machine Learning: Clustering & Retrieval 的評價和反饋

4.6
1,828 個評分
314 條評論

課程概述

Case Studies: Finding Similar Documents A reader is interested in a specific news article and you want to find similar articles to recommend. What is the right notion of similarity? Moreover, what if there are millions of other documents? Each time you want to a retrieve a new document, do you need to search through all other documents? How do you group similar documents together? How do you discover new, emerging topics that the documents cover? In this third case study, finding similar documents, you will examine similarity-based algorithms for retrieval. In this course, you will also examine structured representations for describing the documents in the corpus, including clustering and mixed membership models, such as latent Dirichlet allocation (LDA). You will implement expectation maximization (EM) to learn the document clusterings, and see how to scale the methods using MapReduce. Learning Outcomes: By the end of this course, you will be able to: -Create a document retrieval system using k-nearest neighbors. -Identify various similarity metrics for text data. -Reduce computations in k-nearest neighbor search by using KD-trees. -Produce approximate nearest neighbors using locality sensitive hashing. -Compare and contrast supervised and unsupervised learning tasks. -Cluster documents by topic using k-means. -Describe how to parallelize k-means using MapReduce. -Examine probabilistic clustering approaches using mixtures models. -Fit a mixture of Gaussian model using expectation maximization (EM). -Perform mixed membership modeling using latent Dirichlet allocation (LDA). -Describe the steps of a Gibbs sampler and how to use its output to draw inferences. -Compare and contrast initialization techniques for non-convex optimization objectives. -Implement these techniques in Python....

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BK

Aug 25, 2016

excellent material! It would be nice, however, to mention some reading material, books or articles, for those interested in the details and the theories behind the concepts presented in the course.

JM

Jan 17, 2017

Excellent course, well thought out lectures and problem sets. The programming assignments offer an appropriate amount of guidance that allows the students to work through the material on their own.

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276 - Machine Learning: Clustering & Retrieval 的 300 個評論(共 302 個)

創建者 Ramesh S

Aug 22, 2018

The clustering course covered a lot of topics, and it seemed a bit hurried too. I felt the quizzes could have been better worded to make it less confusing. LDA in particular deserved a better treatment - more could have been done I thought in terms of explaining the mathematics as well as the intuition (relative to MoG). Overall, it was a good course, but the best way to judge this would have been to ask a question like this - "what if people did clustering and retrieval even before they did other modules (regression and classification) - would the faculty have dealt the subject in the same way? ". My guess, is "unlikely" and that kinda explains what was missing !

創建者 Saeed S T

Sep 08, 2016

Overall a good and useful course, however:

A) They could do a much better job regarding LDA, standard Gibbs sampling, and Bayesian model and inference. Many slides on these 3 topics only contained some text and the instructor tried to "verbally" visualize the related important concepts. Hence not a good use of a video session.

B) Week 1 and the 1st half of Week 6 were redundant.

C) It would be much better to have a 7-week course with more topics and may be with some optional videos on Bayesian model, HMM.

創建者 Adrien S

Oct 07, 2016

Feels like this course in the specialization was a bit rushed, compared to the first 3 courses. It had 2 modules (first & last) that were more like placeholders and the middle 4 modules went from concept to the maths behind the algorithm very quickly. It needs a bit of work on expanding the course and presenting a bit more slowly. Having said all that, the concepts and algorithms taught are very interesting and a good first step into the unsupervised learning section.

創建者 Oliverio J S J

Jun 20, 2018

Some of the contents of this course are interesting, but it seems that this course has been very affected by the changes that forced the cancellation of the last two courses of the specialization. Apparently, they had to redo it and there are even two fake weeks (the first one and the last one). It is a pity that they did not want to spend more time to reorganize it.

創建者 Ahmed N

Jul 18, 2017

The course focus on a great part of researches i have never read about them or had any idea about all of it. It doesn't focus on how we implement the core functions of machine learning but it was all of benefits and very very good to me i have learned a lot of things thank you all it's very tough and challenging course for me thank you all.

創建者 Dmitri B

Jun 21, 2017

Theory is cool but programming assignments requires proficient phyton knowledge. GraphLab helps but it wont be used in real life in our company :(

I found strange that often optional topics are major part of quiz, but anyway you should watch everything :)

創建者 Dimitrios Z

Jun 08, 2019

It has intresting theory but I believe the exercises need to be improvised. Maybe using Jupyter online and guiding the student to write code to solve the problems. In conclusion, I understood the basic theory but mostly that.

創建者 Kayvan S

Feb 15, 2018

Great course but I think the workload could be spread across the weeks more. Also, I had a lot of trouble with the sklearn toolkit (probably due to installation issues.).

創建者 Piotr Ś

Feb 15, 2017

Dependence on GraphLab technology is a big minus. The lectures are poorly balanced in terms of difficulty. Apart from that - interesting course, I'm glad I took it.

創建者 Aayush G

Nov 10, 2016

This specific course traded off depth and detail for breadth of topics. Too many ideas were quickly described and not really built up to my liking.

創建者 Pavan B

Jul 29, 2019

Few concepts were covered in hurry with lot of concepts described abruptly. It took a while for me to do research about those topics to catchup.

創建者 Alexander S

Aug 07, 2016

great course, but module 4 lacks a bit in structure. hard to follow. without the forum, it would not be possible to make it in time.

創建者 Baubak G

Jul 11, 2018

Need more details in the coarse. I think many of the topics need more working on, and are not sufficiently described.

創建者 Valentina S

Aug 16, 2016

Interesting content but explanations are less clear with respect to the other courses of the ML Specialization

創建者 Michael L

Mar 18, 2017

slightly repetitive of classification course with no real use-case value except lots of math..

創建者 Volker H

Jul 18, 2016

please rework in particular week 5, part 2

創建者 Nicolas I

Aug 31, 2016

A little too superficial and hand waving.

創建者 Harsh A

Jul 18, 2018

Too little "case-study" approach

創建者 Xiaosong L

Aug 31, 2016

the homework is getting easy

創建者 Pan W

Jan 03, 2017

I give 5 star for the teacher, really approach having such a well-organized teaching material.

I also give -1 star for the homework assignment and its (almost) GraphLab only approach. Yes, it mentioned "alternative" approach (which is much more popular than GraphLab), but there are many bugs & trivial difficulties to get it through. With scikit-learn as a great open source package, the only reason (I suspect) to choose GraphLab is commercial purpose. For me, if the homework assignment is only instructed properly for loading data into Pandas, I can finish each programming assignment within 1 hour for sure using scikit learn; but now, it takes 30 minutes and I still cannot load the data correctly. I like to get a certificate, but it is not necessary and spending too much time is a waste on my time.

創建者 ryan

Sep 23, 2017

requires use of a programming library from a company that was sold and is unmaintained. Challenging to build the environment to run the homework code on my mac pro. An AMI is provided so you can try to do the assignments on a prebuilt machine. Anyway I've found the class quite a hassle.

創建者 Dario D G

Jan 19, 2020

Organized decently, yet tools such as TuriCreate have been associated to a lot of problems with running the assignments. Additionally, it seemed very difficult to receive any sort of assistance if stuck with an assignment or tool.

創建者 Karl S

Oct 11, 2016

For me, this course was disappointing. Here is why: First, the level, at which the course material is presented, is very low. It might be freshman level, but certainly not more. There are many buzzwords but no real explanations. The programming assignments are only doable because most of the work has been done by the people designing the assignments. There is very little left for the students. Furthermore, the procedures, that are already given, are not very well documented. Hence, a lot of guess work is required to figure out how things should work. Furthermore, little effort has been spent to structure the procedures that are already given. Altogether, this makes doing the programming assignments very unsatisfying.

Finally, the professor presenting the materials does not take part in the discussion forums. Contrary to other courses that I have attended at Coursera, this time the discussion forum was no help at all.

創建者 Kripakaran R

Nov 12, 2018

I wish week4 and week5 were better. It felt so rushed, where most of the important things were covered.

創建者 Andreas

Jan 04, 2017

This specialization is delayed for months now - very annoying! Don't give them money!