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Machine Learning: Clustering & Retrieval に戻る

Machine Learning: Clustering & Retrieval, ワシントン大学(University of Washington)



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....


by 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.

by 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.



by Dennis Sivia

May 19, 2019

Amazing course. The Instructors did an awesome job of preparing and presenting the material.

I think there is no better and more approachable in-depth course out there. Thank you so much!

by Jafed Encinas

May 14, 2019

Able to concentrate and stay focused for periods of several hours, even when tasks are relatively mundane, and doesn't make mistakes. He has a high boredom threshold. Always assured and confident in demeanour and presentation of ideas without being aggressively over-confident. No absences without valid reason in 6 months. Reaches a decision rapidly after taking account of all likely outcomes and estimating the route most likely to bring success. The decisions almost always turn out to be good ones.

This Course always completes any assignment on time and to a high standard. This Course has outstanding artistic or craft skills, bringing creativity and originality to the task. Aiming for a top job in the organization. He sets very high standards, aware that this will bring attention and promotion. This Course pays great attention to detail. He always presented work properly checked and completely free of error.

by kripa shankar

Apr 30, 2019

One of the best training experience...

by 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.

by Akash Gupta

Mar 11, 2019

Machine Learning: Clustering & Retrieval good and learn easily

by Sathiraju Eswar

Mar 03, 2019

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

by 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

by Edwin Pucuji

Feb 15, 2019

Excellent, good contribution to the technical and practical knowledge ML

by Zhongkai Mi

Feb 12, 2019

Great assignments : )

by 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!