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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
2,096 個評分
360 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....

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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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201 - Machine Learning: Clustering & Retrieval 的 225 個評論(共 351 個)

創建者 Juan F H

Nov 15, 2018

The teacher is awesome

創建者 gaozhipeng

Dec 27, 2016

VERY IMPRESSIVE COURSE

創建者 Zhongkai M

Feb 12, 2019

Great assignments : )

創建者 roi s

Oct 29, 2017

Great, very hands on!

創建者 Weituo H

Aug 29, 2016

strongly recommended!

創建者 Sukhvir S

Jul 10, 2020

wonderful experience

創建者 Omar S

Jul 12, 2017

I loved this course!

創建者 Itrat R

Jan 23, 2017

Excellent Course!!!

創建者 Ms. B P A P I - C

Sep 29, 2020

VERY USEFUL COURSE

創建者 SUBBA R D

Jun 16, 2020

most useful course

創建者 Israel C

Aug 15, 2017

Excellent Course!

創建者 Antonio P L

Oct 03, 2016

Excellent course.

創建者 JiHe

Sep 08, 2016

Very good course!

創建者 Igor D

Aug 21, 2016

This was AWESOME!

創建者 zhenyue z

Aug 09, 2016

very nice lecture

創建者 Anurag

Dec 20, 2019

Great Experience

創建者 Xue

Dec 19, 2018

Great but hard~!

創建者 嵇昊雨

Apr 25, 2017

内容深度可以,对个人的帮助比较大

創建者 Daniel W

Dec 23, 2016

Excellent course

創建者 Sumit

Sep 17, 2016

Excellent course

創建者 Phan T B

Aug 08, 2016

very good course

創建者 Md. K H T

Jul 25, 2020

Awesome Course.

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!