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Machine Learning: Clustering & Retrieval, 华盛顿大学

4.6
1,633 個評分
281 個審閱

課程信息

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....

熱門審閱

創建者 BK

Aug 25, 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.

創建者 JM

Jan 17, 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.

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269 個審閱

創建者 Martin Belder

Apr 11, 2019

Greatly enjoyed it. As with the other courses in this specialization the discussion of the subjects is impeccable, especially if you've taken some preparatory mathematics courses. The reliance on Graphlab Create is a drag though.

創建者 Akash Gupta

Mar 11, 2019

Machine Learning: Clustering & Retrieval good and learn easily

創建者 Sathiraju Eswar

Mar 03, 2019

Very nice course. Things are well explained, however some concepts could be expanded more.

創建者 Jialie (Julie) Yan

Feb 21, 2019

The course is really helpful, though it would be better for teacher to illustrate the concepts by using examples, instead of abstract terminologies

創建者 Edwin Pucuji

Feb 15, 2019

Excellent, good contribution to the technical and practical knowledge ML

創建者 Zhongkai Mi

Feb 12, 2019

Great assignments : )

創建者 Vikash Singh Negi

Feb 03, 2019

It was great but I was also interested to implement the solutions with pyspark...though I did it eventually. Thank you!

創建者 Srinivas CS

Jan 07, 2019

This was a really good course, It made me familiar with many tools and techniques used in ML. With this in hand I will be able to go out there and explore and understand things much better.

創建者 Jay Kumar Sinha

Jan 05, 2019

Excellent course material and fantastic delivery. You guys made this complex learning so simple and interesting . Thanks for all this, keep the good works.

創建者 KAI NIU

Jan 03, 2019

Excellent course with great and reachable explanation