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返回到 Machine Learning: Clustering & Retrieval

学生对 University of Washington 提供的 Machine Learning: Clustering & Retrieval 的评价和反馈

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2,366 个评分

课程概述

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....

热门审阅

TT

Oct 29, 2016

I really learn a lot in this course, although the materials are very difficult at first read, but Emily's explanation were clear and I would be able to get the idea after a few review.

CS

Feb 11, 2020

Excellent Course. This course provides in depth understanding of what's going in the background when an algorithm runs and how we can tune it for our purpose.

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226 - Machine Learning: Clustering & Retrieval 的 250 个评论（共 392 个）

创建者 Roi S

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Oct 29, 2017

Great, very hands on!

创建者 Weituo H

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Aug 28, 2016

strongly recommended!

创建者 Sukhvir S

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Jul 10, 2020

wonderful experience

创建者 Omar S

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Jul 12, 2017

I loved this course!

创建者 Itrat R

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Jan 22, 2017

Excellent Course!!!

创建者 PAVITHRA B

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Sep 29, 2020

VERY USEFUL COURSE

创建者 SUBBA R D

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Jun 15, 2020

most useful course

创建者 Israel C

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Aug 15, 2017

Excellent Course!

创建者 Antonio P L

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Oct 3, 2016

Excellent course.

创建者 Ji H

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Sep 7, 2016

Very good course!

创建者 Igor D

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Aug 21, 2016

This was AWESOME!

创建者 zhenyue z

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Aug 8, 2016

very nice lecture

创建者 ANKIT N

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Jul 25, 2023

very good course

创建者 Anurag B

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Dec 19, 2019

Great Experience

创建者 Xue

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Dec 18, 2018

Great but hard~!

创建者 Haoyu J

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Apr 25, 2017

内容深度可以，对个人的帮助比较大

创建者 Daniel W

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Dec 23, 2016

Excellent course

创建者 Sumit

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Sep 16, 2016

Excellent course

创建者 Phan T B

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Aug 8, 2016

very good course

创建者 Md. K H T

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Jul 24, 2020

Awesome Course.

创建者 IDOWU H A

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May 20, 2018

Excellent - Goo

创建者 vivek k

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May 24, 2017

awesome course!

创建者 Bruno G E

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Sep 3, 2016

Simply Amazing!

创建者 Christopher D

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Aug 8, 2016

Superb course!

创建者 Jinho L

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Sep 19, 2016

Great! thanks