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

4.7
stars
2,357 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

BK

Aug 24, 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 16, 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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251 - 275 of 389 Reviews for Machine Learning: Clustering & Retrieval

By Pakomius Y N

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Sep 28, 2020

Terima Kasih

By Divyanshu S

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Aug 27, 2020

Very helpful

By JOYDIP M

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Jul 30, 2020

very helpful

By Manikant R

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Jun 21, 2020

Great course

By ANKUR S

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Apr 14, 2020

loved it..!!

By Hanna L

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Sep 2, 2019

Great class!

By Mark h

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Aug 8, 2017

Very helpful

By 邓松

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Jan 4, 2017

very helpful

By Jiancheng Y

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Oct 26, 2016

Great intro!

By Thuong D H

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Sep 22, 2016

Good course!

By Karundeep Y

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Sep 18, 2016

Best Course.

By Prathibha A

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Dec 6, 2021

good course

By Siddharth V B

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Nov 29, 2020

nice course

By Saurabh A

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Sep 24, 2020

very good !

By Pradeep N

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Feb 21, 2017

"super one,

By clark.bourne

•

Jan 8, 2017

内容丰富实际，材料全。

By Salim T T

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Apr 27, 2021

Thank you!

By VITTE

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Nov 11, 2018

Excellent.

By Gautam R

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Oct 7, 2016

Awesome :)

By VARUN K

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Sep 19, 2023

VERY NICE

By miguel s

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Sep 20, 2020

very well

By Neha K

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Sep 19, 2020

EXCELLENT

By PAWAN S

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Sep 17, 2020

excellent

By Subhadip P

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Aug 4, 2020

excellent

By Alan B

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Jul 3, 2020

Excellent