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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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2,367 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.

RG

Sep 8, 2017

Good presentation of topics. Detailed walk through of few advanced topics covered at the end would have been great. Felt the presentation went too fast.

筛选依据：

251 - Machine Learning: Clustering & Retrieval 的 275 个评论（共 392 个）

创建者 Rojas A R

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Nov 27, 2025

Good course!

创建者 Sumit K J

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Jan 24, 2021

Great Course

创建者 Pakomius Y N

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Sep 28, 2020

Terima Kasih

创建者 Divyanshu S

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Aug 27, 2020

Very helpful

创建者 JOYDIP M

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Jul 30, 2020

very helpful

创建者 Manikant R

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Jun 20, 2020

Great course

创建者 ANKUR S

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Apr 14, 2020

loved it..!!

创建者 Hanna L

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Sep 1, 2019

Great class!

创建者 Mark h

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Aug 7, 2017

Very helpful

创建者 邓松

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Jan 3, 2017

very helpful

创建者 Jiancheng Y

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Oct 26, 2016

Great intro!

创建者 Thuong D H

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Sep 22, 2016

Good course!

创建者 Karundeep Y

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Sep 18, 2016

Best Course.

创建者 Prathibha A

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Dec 6, 2021

good course

创建者 Siddharth V B

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Nov 29, 2020

nice course

创建者 Saurabh A

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Sep 24, 2020

very good !

创建者 Dr P S N

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Feb 21, 2017

"super one,

创建者 clark.bourne

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Jan 8, 2017

内容丰富实际，材料全。

创建者 Salim T T

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Apr 27, 2021

Thank you!

创建者 VITTE

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Nov 11, 2018

Excellent.

创建者 Gautam R

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Oct 7, 2016

Awesome :)

创建者 VARUN K

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Sep 19, 2023

VERY NICE

创建者 miguel s

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Sep 20, 2020

very well

创建者 Neha K

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Sep 19, 2020

EXCELLENT

创建者 PAWAN S

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Sep 16, 2020

excellent