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

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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

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.

KK

Sep 7, 2017

Great course, all the explanations are so good and well explained in the slides. Programming assignments are pretty challenging, but give really good insight into the algorithms!.Thanks!

筛选依据：

151 - Machine Learning: Clustering & Retrieval 的 175 个评论（共 390 个）

创建者 Akash G

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Mar 11, 2019

Machine Learning: Clustering & Retrieval good and learn easily

创建者 Shaonan W

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Nov 20, 2016

Deep insight into most useful techniques of machine learning.

创建者 JOSE R

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Nov 18, 2017

Very well explained. The LDA was difficult to learn. Thanks.

创建者 Daniel R

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Aug 16, 2016

Another great hit by Emily and Carlos!!! Excellent Course!!!

创建者 Yifei L

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Jul 30, 2016

Good course for KD trees, LSH, Gaussian mixed model and LDA.

创建者 Victor C

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Jun 24, 2017

Excellent teacher and material. I wish there were more...

创建者 Guillermo O d A

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Jun 4, 2022

Excellent course. I am looking forward for a second part.

创建者 Francisco R M

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Mar 19, 2021

Too many assingments dedicated to on scratch development.

创建者 Moayyad Y

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Dec 4, 2016

this is not a an easy course but certainly an awesome one

创建者 Fengchen G

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Sep 1, 2016

Awesome course! The session on EM algorithm is revealing!

创建者 Divyang S

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Sep 13, 2020

Excellent content... Really intuitive and well explained

创建者 Yong D K

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May 7, 2018

This is the best course for Information Retrieval ever!

创建者 Sameer M

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Sep 19, 2017

Excellent course! must for machine learning beginners!!

创建者 陈佳艺

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May 17, 2017

sometimes difficult,but import so many useful knowledge

创建者 Joseph P

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Jan 16, 2017

Very sophisticated, friendly and practical instructions

创建者 Manoj K

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Nov 25, 2018

session was very helpful & full with relevant contents

创建者 Siwei Y

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Jan 17, 2017

本来不报什么期望，但是该门课确实做得相当好。　相信该课的老师们花了巨大的心血。真的是业界良心。所以强烈点赞。

创建者 Oleg B

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Dec 3, 2016

Great course, very hands-on, very practical knowledge.

创建者 Niu K

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Jan 3, 2019

Excellent course with great and reachable explanation

创建者 Vladimir V

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Jun 26, 2017

Awesome course. Thank you Emily, Carlos and Coursera!

创建者 Kishore P V R

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Oct 5, 2016

One of the best machine learning course I have taken.

创建者 Jaswant J

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Mar 31, 2017

Very nice course. Concepts are covered very clearly.

创建者 Yang X

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Nov 14, 2017

Thank you Emily and Carlos! You guys are amazing!!!

创建者 Sean L

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Oct 4, 2016

wonderful course for beginner of machine learning.

创建者 Banka C G

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Aug 10, 2019

Its my great experience for step by step modules