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学生对 华盛顿大学 提供的 Machine Learning: Clustering & Retrieval 的评价和反馈

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392 条评论


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....



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.


Jan 16, 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.


176 - Machine Learning: Clustering & Retrieval 的 200 个评论(共 380 个)

创建者 Arash A

Jan 5, 2017

Enjoyed the course and learned a lot. Amazing!

创建者 David F

Oct 21, 2016

Excellent course - and of great practical use.

创建者 Nitish V

Oct 29, 2017

The Course is good . Covered lots of topics .

创建者 Rahul G

Jun 13, 2017

Good course but Week 5 LDA needs improvement.

创建者 Stanislav B

Apr 15, 2020

one of the best courses Ive seen on coursera

创建者 Jason G

Aug 9, 2017

Harder than the previous ones, but enjoyable

创建者 Krisda L

Jul 19, 2017

Good overview of a lot of useful techniques.

创建者 felix a f a

Aug 8, 2016

less complex exercises to check and validate

创建者 Feiwen C ( C I

Jun 1, 2017

Good course. Learned a lot from it. Thanks!

创建者 Kan C Y

Mar 19, 2017

Really a good course, succinct and concise.

创建者 parag_verma

Jan 7, 2020

Thanks to the entire team of this course.


Dec 27, 2018

Nice content and well made presentations.

创建者 Miao J

Jul 1, 2016

Another great course. Strongly recommend!

创建者 Veer A S

Mar 23, 2018

Very informative and interesting course.

创建者 Ted T

Jul 29, 2017

Best ML course ever. Easy to understand!

创建者 Dmitri T

Dec 4, 2016

Great course! Very simple and practical.

创建者 Veera K R

Apr 6, 2020

Very informative and Clearly explained.

创建者 Snehotosh B

Dec 3, 2016

Best course available till date as MooC

创建者 kripa s

Apr 30, 2019

One of the best training experience...

创建者 Shuang D

Jun 29, 2018

advanced knowledge on ML, great course

创建者 Garvish

Jun 14, 2017

Great Information and organised course


Sep 21, 2020

Everything was very clearly explained

创建者 Ce J

Jun 26, 2017

well organized and easy to understand

创建者 李紹弘

Aug 22, 2017

This course provides concise course.

创建者 Nada M

Jun 11, 2017

Thank you! I loved all your classes.