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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
1,916 個評分
327 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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301 - Machine Learning: Clustering & Retrieval 的 316 個評論(共 316 個)

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 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!

創建者 Adrien L

Feb 02, 2017

No good without the missing course and capstone projects

創建者 Ken C

Feb 04, 2017

Not happy about course 5 & 6 got cancelled.