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Learner Reviews & Feedback for Machine Learning: Clustering & Retrieval by University of Washington

4.6
stars
2,366 ratings

About the Course

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....

Top reviews

TT

Oct 29, 2016

I really learn a lot in this course, although the materials are very difficult at first read, but Emily's explanation were clear and I would be able to get the idea after a few review.

PK

Sep 7, 2017

Great course, all the explanations are so good and well explained in the slides. Programming assignments are pretty challenging, but give really good insight into the algorithms!.Thanks!

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276 - 300 of 392 Reviews for Machine Learning: Clustering & Retrieval

By Subhadip P

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Aug 4, 2020

excellent

By Alan B

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Jul 2, 2020

Excellent

By DHRUV S

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Nov 4, 2023

good one

By Iñigo C S

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Aug 8, 2016

Amazing.

By Mr. J

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May 22, 2020

Superb.

By Zihan W

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Aug 21, 2020

great~

By Bingyan C

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Dec 26, 2016

great.

By Cuiqing L

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Nov 5, 2016

great!

By Job W

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Jul 23, 2016

Great!

By Vyshnavi G

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Jan 23, 2022

super

By SUJAY P

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Aug 21, 2020

great

By Sarthak S

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Nov 5, 2024

nice

By Krish G

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Sep 7, 2024

NICE

By Badisa N

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Jan 27, 2022

good

By Vaibhav K

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Sep 29, 2020

good

By Pritam B

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Aug 13, 2020

well

By Frank

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Nov 23, 2016

非常棒！

By Pavithra M

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May 24, 2020

nil

By Alexander L

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Oct 23, 2016

ok

By Nagendra K M R

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Nov 10, 2018

G

By Suneel M

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May 8, 2018

E

By Lalithmohan S

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Mar 26, 2018

V

By Ruchi S

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Jan 23, 2018

E

By Kevin C N

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Mar 26, 2017

E