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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
1,840 個評分
316 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....

熱門審閱

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.

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.

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151 - Machine Learning: Clustering & Retrieval 的 175 個評論(共 304 個)

創建者 David F

Oct 21, 2016

Excellent course - and of great practical use.

創建者 Nitish V

Oct 29, 2017

The Course is good . Covered lots of topics .

創建者 Rahul G

Jun 13, 2017

Good course but Week 5 LDA needs improvement.

創建者 Jason G

Aug 09, 2017

Harder than the previous ones, but enjoyable

創建者 Krisda L

Jul 19, 2017

Good overview of a lot of useful techniques.

創建者 felix a f a

Aug 08, 2016

less complex exercises to check and validate

創建者 Feiwen C ( C I

Jun 02, 2017

Good course. Learned a lot from it. Thanks!

創建者 Kan C Y

Mar 19, 2017

Really a good course, succinct and concise.

創建者 parag_verma

Jan 07, 2020

Thanks to the entire team of this course.

創建者 PRAVEEN R U

Dec 27, 2018

Nice content and well made presentations.

創建者 Miao J

Jul 01, 2016

Another great course. Strongly recommend!

創建者 Veer A S

Mar 24, 2018

Very informative and interesting course.

創建者 Ted T

Jul 29, 2017

Best ML course ever. Easy to understand!

創建者 Dmitri T

Dec 05, 2016

Great course! Very simple and practical.

創建者 Snehotosh K B

Dec 03, 2016

Best course available till date as MooC

創建者 kripa s

Apr 30, 2019

One of the best training experience...

創建者 Shuang D

Jun 29, 2018

advanced knowledge on ML, great course

創建者 Garvish

Jun 14, 2017

Great Information and organised course

創建者 Ce J

Jun 26, 2017

well organized and easy to understand

創建者 李紹弘

Aug 22, 2017

This course provides concise course.

創建者 Nada M

Jun 11, 2017

Thank you! I loved all your classes.

創建者 Fernando B

Feb 21, 2017

Best Course on ML yet on the Web

創建者 Matheus F

Aug 11, 2018

Excelent course! Very helpful!

創建者 Foo C S G

Mar 04, 2018

Tough slog, but well designed

創建者 Roger S

Sep 04, 2016

Worth the wait. COOL learning