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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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201 - 225 of 389 Reviews for Machine Learning: Clustering & Retrieval

By 李紹弘

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Aug 22, 2017

This course provides concise course.

By Nada M

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Jun 11, 2017

Thank you! I loved all your classes.

By Fernando B

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Feb 21, 2017

Best Course on ML yet on the Web

By AANCHAL K

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Oct 17, 2020

It was brelient , just no words

By Matheus F

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Aug 10, 2018

Excelent course! Very helpful!

By Ritik R S

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Jun 3, 2022

Thank you so much! I love it.

By Foo C S G

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Mar 4, 2018

Tough slog, but well designed

By Roger S

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Sep 4, 2016

Worth the wait. COOL learning

By dan o

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Dec 6, 2016

Thank you, it was a good one

By Sandeep J

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Sep 4, 2016

Best course I've taken!! :)

By Nirmal M

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Jan 22, 2022

very helpful and inovating

By Alessandro B

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Dec 15, 2017

very useful and structured

By Adapa S K

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Jul 23, 2022

quality ofcontent is good

By wonjai c

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May 19, 2020

difficult but good enough

By Mostafa A

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Aug 28, 2016

Fantastic course as usual

By Gaurav K

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Sep 23, 2020

very good course to do.

By Jay M

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May 26, 2020

Very good course for ML

By Velpula M K

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Dec 6, 2019

Good and best to learn.

By Brian N

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May 20, 2018

This course is exciting

By Suryatapa R

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Dec 16, 2016

It's an amazing Course.

By Aishwarya A

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Nov 28, 2020

best place to learn ML

By Juan F H Z

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Nov 15, 2018

The teacher is awesome

By gaozhipeng

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Dec 26, 2016

VERY IMPRESSIVE COURSE

By zhongkai m

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Feb 12, 2019

Great assignments : )

By roi s

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Oct 29, 2017

Great, very hands on!