Chevron Left
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

4.6
星
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：

276 - Machine Learning: Clustering & Retrieval 的 300 个评论（共 392 个）

创建者 Subhadip P

•

Aug 4, 2020

excellent

创建者 Alan B

•

Jul 2, 2020

Excellent

创建者 DHRUV S

•

Nov 4, 2023

good one

创建者 Iñigo C S

•

Aug 8, 2016

Amazing.

创建者 Mr. J

•

May 22, 2020

Superb.

创建者 Zihan W

•

Aug 21, 2020

great~

创建者 Bingyan C

•

Dec 26, 2016

great.

创建者 Cuiqing L

•

Nov 5, 2016

great!

创建者 Job W

•

Jul 23, 2016

Great!

创建者 Vyshnavi G

•

Jan 23, 2022

super

创建者 SUJAY P

•

Aug 21, 2020

great

创建者 Sarthak S

•

Nov 5, 2024

nice

创建者 Krish G

•

Sep 7, 2024

NICE

创建者 Badisa N

•

Jan 27, 2022

good

创建者 Vaibhav K

•

Sep 29, 2020

good

创建者 Pritam B

•

Aug 13, 2020

well

创建者 Frank

•

Nov 23, 2016

非常棒！

创建者 Pavithra M

•

May 24, 2020

nil

创建者 Alexander L

•

Oct 23, 2016

ok

创建者 Nagendra K M R

•

Nov 10, 2018

G

创建者 Suneel M

•

May 8, 2018

E

创建者 Lalithmohan S

•

Mar 26, 2018

V

创建者 Ruchi S

•

Jan 23, 2018

E

创建者 Kevin C N

•

Mar 26, 2017

E