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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
1,845 個評分
317 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

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.

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.

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201 - Machine Learning: Clustering & Retrieval 的 225 個評論(共 305 個)

創建者 IDOWU H A

May 20, 2018

Excellent - Goo

創建者 vivek k

May 25, 2017

awesome course!

創建者 Bruno G E

Sep 03, 2016

Simply Amazing!

創建者 Christopher D

Aug 09, 2016

Superb course!

創建者 Jinho L

Sep 20, 2016

Great! thanks

創建者 Hanna L

Sep 02, 2019

Great class!

創建者 Mark h

Aug 08, 2017

Very helpful

創建者 邓松

Jan 04, 2017

very helpful

創建者 Jiancheng

Oct 27, 2016

Great intro!

創建者 Thuong D H

Sep 23, 2016

Good course!

創建者 Karundeep Y

Sep 18, 2016

Best Course.

創建者 Pradeep N

Feb 22, 2017

"super one,

創建者 clark.bourne

Jan 09, 2017

内容丰富实际,材料全。

創建者 VITTE

Nov 11, 2018

Excellent.

創建者 Gautam.R

Oct 08, 2016

Awesome :)

創建者 RISHABH T

Nov 12, 2017

excellent

創建者 Iñigo C S

Aug 08, 2016

Amazing.

創建者 Bingyan C

Dec 27, 2016

great.

創建者 Cuiqing L

Nov 05, 2016

great!

創建者 Job W

Jul 23, 2016

Great!

創建者 Frank

Nov 23, 2016

非常棒!

創建者 Alexandre

Oct 23, 2016

ok

創建者 Nagendra K M R

Nov 11, 2018

G

創建者 Suneel M

May 09, 2018

E

創建者 Lalithmohan S

Mar 26, 2018

V