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Kursteilnehmer-Bewertung und -Feedback für Machine Learning: Clustering & Retrieval von University of Washington

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376 Bewertungen

Über den Kurs

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....

Top-Bewertungen

BK
24. Aug. 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.

JM
16. Jan. 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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201 - 225 von 364 Bewertungen für Machine Learning: Clustering & Retrieval

von Alessandro B

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15. Dez. 2017

very useful and structured

von wonjai c

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19. Mai 2020

difficult but good enough

von Moustafa A A

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28. Aug. 2016

Fantastic course as usual

von Gaurav K

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23. Sep. 2020

very good course to do.

von Jay M

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26. Mai 2020

Very good course for ML

von Velpula M K

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6. Dez. 2019

Good and best to learn.

von Brian N

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20. Mai 2018

This course is exciting

von suryatapa r

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16. Dez. 2016

It's an amazing Course.

von Aishwarya A

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28. Nov. 2020

best place to learn ML

von Juan F H

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15. Nov. 2018

The teacher is awesome

von gaozhipeng

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26. Dez. 2016

VERY IMPRESSIVE COURSE

von Zhongkai M

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12. Feb. 2019

Great assignments : )

von roi s

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29. Okt. 2017

Great, very hands on!

von Weituo H

•
29. Aug. 2016

strongly recommended!

von Sukhvir S

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10. Juli 2020

wonderful experience

von Omar S

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12. Juli 2017

I loved this course!

von Itrat R

•
22. Jan. 2017

Excellent Course!!!

von PAVITHRA B

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29. Sep. 2020

VERY USEFUL COURSE

von SUBBA R D

•
16. Juni 2020

most useful course

von Israel C

•
15. Aug. 2017

Excellent Course!

von Antonio P L

•
3. Okt. 2016

Excellent course.

von jihe

•
8. Sep. 2016

Very good course!

von Igor D

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21. Aug. 2016

This was AWESOME!

von zhenyue z

•
9. Aug. 2016

very nice lecture

von Anurag

•
20. Dez. 2019

Great Experience

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