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

4.6
stars
2,229 ratings
381 reviews

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 370 Reviews for Machine Learning: Clustering & Retrieval

By Jiancheng

Oct 26, 2016

Great intro!

By Thuong D H

Sep 22, 2016

Good course!

By Karundeep Y

Sep 18, 2016

Best Course.

By Siddharth V B

Nov 29, 2020

nice course

By Saurabh A

Sep 24, 2020

very good !

By Pradeep N

Feb 21, 2017

"super one,

By clark.bourne

Jan 8, 2017

内容丰富实际,材料全。

By Salim T T

Apr 27, 2021

Thank you!

By VITTE

Nov 11, 2018

Excellent.

By Gautam R

Oct 8, 2016

Awesome :)

By miguel s

Sep 20, 2020

very well

By Neha K

Sep 19, 2020

EXCELLENT

By PAWAN S

Sep 17, 2020

excellent

By Subhadip P

Aug 4, 2020

excellent

By Alan B

Jul 3, 2020

Excellent

By RISHABH T

Nov 12, 2017

excellent

By Iñigo C S

Aug 8, 2016

Amazing.

By Mr. J

May 22, 2020

Superb.

By Zihan W

Aug 21, 2020

great~

By Bingyan C

Dec 26, 2016

great.

By Cuiqing L

Nov 5, 2016

great!

By Job W

Jul 23, 2016

Great!

By SUJAY P

Aug 21, 2020

great

By Vaibhav K

Sep 29, 2020

good

By Pritam B

Aug 13, 2020

well